9th Biennial ACSPRI Social Science Methodology Conference

This workshop is aimed at anyone who develops and refines survey questionnaires and wants to ensure their survey measures have validity. The workshop will make the case for the value of cognitive testing as part of the questionnaire development process by looking at a few real-world examples where the cognitive testing process resulted in critical improvements being made to a survey questionnaire. Following this, the workshop will provide an outline and framework for how to conduct a cognitive testing program. In addition to this, the workshop will look at some key tips to enhance the likelihood of a successful cognitive testing program. Draft workshop subsections include: - Why do cognitive testing? - A framework for cognitive testing (developed by Roger Tourangeau, 1984) - An outline of how to conduct cognitive testing - Key tips for success - Practice session - participants to break into small groups and conduct a mock cognitive test on each other. Workshop then regroups to discuss learnings.

This workshop will introduce participants to open source R packages for online network collection and analysis, developed by the Virtual Observatory for the Study of Online Networks (VOSON) Lab (http://vosonlab.net) at the Australian National University. The workshop will include an introduction to (depending on workshop participant interest and available API access):

• vosonSML (https://github.com/vosonlab/vosonSML) - an R package providing a suite of tools for collecting and constructing networks from social media data. It provides easy-to-use functions for collecting data across popular platforms (Twitter, Reddit, YouTube, WWW hyperlinks, Mastodon) and generating different types of networks for analysis.
• VOSON Dashboard (https://github.com/vosonlab/VOSONDash) - an R/Shiny application providing a graphical user interface for collecting and analysing online networks and associated text data. It builds on a number of R packages, in particular igraph (for network analysis) and vosonSML.

Participants will be shown how to install these packages and their basic operation. Workshop materials will include R scripts, package documentation, notes on analysis of online networks, and examples of research.

Objective: The primary objective of this workshop is to equip participants with the knowledge and skills necessary to recognize and utilize mixed methods research designs effectively. Participants will gain a comprehensive understanding of mixed methods design, analysis, and integration. Scientific Content: The workshop will cover: 1. Types of Mixed Methods: Participants will learn about the various types of mixed methods research designs and their applications. 2. Principles of Mixed Methods Design: Essential principles governing mixed methods research design will be discussed to provide participants with a solid foundation. 3. Real-Life Examples: Through case studies and examples, participants will explore how mixed methods research is applied in diverse fields. 4. Mixed Methods Analysis: Techniques for analyzing mixed methods data, including transformation synthesis and integration, will be elucidated. Knowledge: By the end of the workshop, participants will be able to recognize different types of mixed methods designs and understand the principles guiding their implementation. Skills: Participants will develop the ability to apply various types of analysis and integration techniques to mixed methods research, enhancing their research capabilities. The workshop will be interactive, engaging participants through presentations, discussions, and hands-on activities to facilitate learning and skill development.

Traditional methods for making estimates from surveys rely on random (probability) samples and high response rates. As the costs of such surveys increase and as response rates decrease, the principles on which traditional methods are based are becoming harder to justify for samples for which the selection and response mechanisms are unknown (or unknowable). A range of statistical techniques have been developed for these situations, to try and model the selection mechanisms and enable estimates and inferences with respect to the target population.

This workshop will give practical advice and examples on design, weighting and estimation for contemporary surveys that do not meet traditional probability methods assumptions. Topics covered will include the following:
* Design considerations including sample composition, questionnaire items, data quality and benchmarks;
* Methods for estimating base weights, making use of a probability reference sample if one is available;
* Calibration to benchmarks; and
* Bias and variance assessment.

The conference will be opened by an ACSPRI Executive Committee member. A Welcome to Country will be performed. The conference sponsors will be recognised. The first plenary speaker will be introduced.

Abstract to come

In this research we argue that large language models (LLMs) and AI-generated content are reshaping information dissemination, belief formation, and social discourse, influencing public opinion research and debate. By enabling rapid content creation, LLMs enhance knowledge access but can also spread misinformation, complicating the task of distinguishing genuine sentiment from AI-driven narratives. Additionally, personalized content recommendations foster echo chambers, reinforcing users' pre-existing beliefs and skewing perceptions of public sentiment. This poses challenges for public opinion researchers aiming to capture representative insights. AI-generated content also impacts social discourse by automating responses and fuelling debates, which can polarize conversations and amplify specific viewpoints. Public opinion research must account for these influences to ensure accurate reflection of societal views. Overall, AI's influence on information flow and belief dynamics requires researchers to adapt methodologies to maintain data integrity and better understand the evolving nature of public discourse.

Storyline writing in a grounded theory study is an advanced coding method. Storylines can help a grounded theorist to develop a simple, parsimonious, explanatory theory of a process. Although generating a storyline typically occurs at the later stages of a grounded theory study, the seeds can be sown systematically throughout the course of the study. In this presentation, a grounded theory study into the process of group singing facilitation will be outlined. Throughout the research, storyline seeds were woven into data generation, data analysis, and reflexivity methods through song. Examples of sowing and nurturing storyline seeds will be provided in this presentation and suggestions will be offered to researchers who wish to harness the nutrients of their own learning style within a robust research design.

Objective. Survey tools are a common approach to gathering data on experiences and opinions, particularly in psychosocial disciplines and public healthcare. In working across language and culture with First Nations peoples, the use of surveys can be challenging, particularly in remote community settings. Via secondary analysis of audio recorded surveys with First Nations mothers we examined conversation occurring between the interviewer and participant during survey administration, to explore implications for the design, delivery, and data quality of quantitative surveys.
Method. Content analysis was applied to 27 audio recorded interviews to determine the function of dialogue occurring between researcher and participant. Initial coding of seven transcripts provided the basis for a coding structure for hypothesis coding of the remaining interviews.
Results. Dialogue between the researcher and the participant predominantly occurred to 1. ensure accurate data via ‘facilitating participant understanding of the item’ and ‘specifying participant response to the item’. Though less frequent, equally important was a process of 2. building connection and understanding via dialogue initiated by the interviewer to ‘explore participant context’ and dialogue initiated by the participant to ‘share personal narratives.’
Conclusions. Findings have implications for the design of surveys, particularly providing space for dialogue. Doing so requires more time, and consequently resources, when conducting surveys with First Nations peoples in remote communities. This mixed-method approach has potential to better the accuracy of data gathered and mitigate the risk of missing responses. Despite reliance in survey research on carefully framed items and Likert scales that typify administration and psychometric thinking, in remote First Nations contexts, social interactions that accompany survey administration represent a process of meaning-making, where the aim is to come to mutual agreement on item responses that 'best fit' the participant.

The integration of Generative AI to support researchers analysing free text responses from public consultations presents an opportunity for rapid and high-quality synthesis for Government. By automating labour-intensive tasks with easily defined rules, such as thematic coding, researchers can focus their time on delivering high-quality qualitative research and critical analysis. However, this approach raises important considerations regarding ethics, privacy, transparency, algorithmic bias, reproducibility, consent, validation, and data quality.

The Social Research Centre uses a range of Gen AI approaches. Using a recent project involving public consultation on a draft cat management strategy delivered for the Victorian Government. This project involved the analysis of 70+ survey questions and 85 written submissions. Generative AI was utilised in the qualitative analysis of 11 free text questions totalling over 8000 responses, in addition to the 85 written submissions totalling 499 pages.

We will discuss the benefits and drawbacks of different Generative AI approaches taken at various stages of the project, sharing valuable learnings gained from these experiences. The presentation will highlight how we navigated the challenges associated with maintaining high standards of security, quality, transparency, and ethics while leveraging the power of AI technologies such as Large Language Models (LLMs).

This study employs arts-based methods to co-create a forum theatre to explore the experiences of young Chinese migrants in Australia and promote mental health literacy. Through a participatory approach grounded in Augusto Boal's Theatre in Education techniques, actors and spectators collaborate to develop and perform a culturally appropriate theatre piece. Phase 1 involves qualitative methods, including group interviews, improvisation, and individual reflections, where actors contribute their personal experiences to shape the theatre script. The co-design process fosters a creative exploration of mental health challenges specific to young Chinese migrants, addressing stigma, barriers to care, and coping strategies. Phase 2 incorporates both qualitative and quantitative data collection from audiences through performances that transition from traditional theatre to interactive problem-solving, encouraging spectators to engage in dialogue and enact potential solutions.
By emphasizing the active participation of both actors and spectators, this research investigates how the co-creation of theatre can affect mental health literacy, attitudes, and behaviours. The use of theatre as a participatory research tool allows for a unique examination of mental health-related knowledge and the lived experiences of participants, offering insights into how arts-based methods can serve as powerful vehicles for social and emotional learning in diverse communities.

Background
Changes in migration patterns to Australia have resulted in significant changes to the range of ethnicities in the population. This is important because the prevalence of blood types differs between people with different ethnic ancestries which can lead to difficulty in meeting the needs of patients who require repeat transfusions. Filipino people have a relatively higher frequency of a rare blood type known as Jknull (Jk(a–b–)), although it is still only present in ~1.4% of that population. Demand for Jknull blood in Australia is increasing, but only 13 current donors in Australia are known to have this blood type. Our aim was to explore the perspectives of donors with Filipino ethnic ancestry on donating blood, and participating in research.
Method
A qualitative approach was used to identify intersubjective meanings and generate explanatory concepts to answer the research questions and inform future work on this topic.
We conducted three focus groups and four interviews with 17 participants online via Microsoft teams. All participants were of Filipino ethnic ancestry. Most participants were from NSW or QLD and the mean age of participants was 42 years. Focus groups were conducted to obtain insights generated through group discussion and interaction on the topics of interest. Interviews were conducted with donors who were not able to participate in the focus groups.
Results
Being part of the Filipino community in Australia was salient for participants and their family and friends. Participants thought community views on blood donation in Australia were informed by systems in the Philippines where donating is seen as a one-off request if there is a need within the family or friends or was seen as a paid activity.
Participants reported feeling as though they were doing something good through blood donation, which was reinforced by the knowledge of who their donations were helping. Participants mentioned obtaining this information through the text messages received after donation, through information received in centre and through knowing blood product recipients. However, lack of education and awareness about the blood donation process, concerns about safety such as the perception that needles were re-used, being worried of needle insertion or weighing less than 50 kilos were seen as barriers towards blood donation.
Participants were positive about participating in research especially if it benefited their community. However, they expressed caution about the privacy and confidentiality of research data, particularly linkage of their research and donation data with external health records and preferred to provide consent for a specific use of their data or samples than to provide broad consent. Participants also voiced their concerns about participating in research run by an organisation other than Lifeblood, particularly research involving the use of genomic data. They thought that understanding how the research would lead to better health outcomes for Filipinos would increase participation. Participants wanted to know how any genomic results collected and returned would be kept confidential, how this information is de-identified and how they would be informed about any incidental findings identified.
Conclusion
Overall, Filipino Australians are open to donating as the idea of helping loved ones and feeling good about doing something helpful was highly appealing. However, participants thought that some individuals would be hesitant to donate blood because they do not know much about the process, have concerns about re-used needles, are afraid of needle insertion, think they might not be eligible, or their blood is not good enough. Participants also discussed that Filipino Australians would be interested in participating in research if they have clear, trustworthy information and feel confident about privacy and the positive impact of their contribution.

Commonwealth governments are expected to set the benchmark for workplace standards. Howeveer there is a significant need for more robust mechanisms to ensure psychological safety to prevent bullying, harassment, and discrimination within their own ranks. This discrepancy between the expectations placed on general workplaces and the practices within the highest levels of government warrants further investigation. This paper aims to explore historic and current approaches in identifying and measuring bullying within organisations, with a focus on Western Commonwealth governments. It has analysed bullying language within the Australian Commonwealth government by labelling three years of Question Time transcripts between 2018-2021 from the House of Parliament, utilising SafeWork Australia's definition of bullying. Additionally, the study assesses the feasibility of using and training Large Language Models (LLMs), RoBERTa, MACAS and ChatGPT-4 to detect bullying language in Hansard data across Commonwealth governments. The LLM's performed well when fine-tuned, with potential for further optimisation to enhance its classification capabilities.
Through these objectives, the research contributes to the field of behavioural science by identifying issues in current measurement approaches, providing empirical evidence on the prevalence of bullying language in the Australian Commonwealth Government, and evaluating the potential of LLMs to enhance real-time detection and prevention of bullying. This study aims to inform future policy changes and improve enforcement mechanisms, ultimately enhancing transparency and accountability within government institutions.

Background
Estimates of forced labour in the Arab States region are conservative due to difficulties in conducting population-based surveys of this target group in this region. Forced labour in the Arab States is expected to be more prevalent in sectors which rely heavily on migrant workers, many of whom do not speak Arabic and reside in locations that are not accessible such as dormitories or employers’ homes. In 2019, the International Labour Organisation estimated the Arab States hosted 24.1 million migrant workers, 14% of the global figure. This study sought to overcome barriers to conducting surveys on forced labour in the Arab States using a novel approach to sample those who had recently worked in the Arab States and since returned to one of six major sending countries.
Aim
The primary aim of this study was to estimate prevalence of forced labour among migrant workers in the Arab States.
Methodology
To achieve the target sample size of 2,000 respondents in each survey country, a hybrid design was used, combining a multi-stage probability design (nationally representative household surveys) with respondent driven sampling (RDS). The target population was persons aged 15 years and older who had worked in an Arab State in the preceding five years, regardless of their type of work (returned migrant workers). The surveys were conducted face-to-face in six countries known to be major source countries for migrants to the Arab States, namely; India, Bangladesh, Pakistan, Indonesia, Philippines, and Ethiopia.
The sampling design aimed to recruit 50 percent of the sample via multi-stage household sampling and 50 percent via RDS to balance the increased efficiency from the RDS sampling with the well-established probability design. RDS “seeds” who referred other members of the target population were identified through the household sampling and by relevant local organisations.
Probability weights were calculated for the household survey sample and calibrated to population benchmarks from the most recent national census. Statistical matching via a nearest neighbours’ method was used to determine appropriate weights for each record in the nonprobability sample by imputing weights from the matched records in the probability sample.
Results
A total of 10,302 returned migrant workers were surveyed, with 48.5 percent recruited via RDS and 51.5 percent recruited via household sampling. Of the total returned migrant workers, 18.5 percent had been in forced labour in an Arab States country in the preceding five years. There was no significant difference in occurrence of forced labour between those recruited by RDS and household sampling. Issues encountered during the collection of the RDS combined with the experimental nature of the approach to weighting the RDS sample limited our ability to produce reliable estimates of the prevalence of forced labour among returned migrant workers from the Arab States.
Conclusion
A hybrid design that combines probability and nonprobability sampling approaches offers the potential to recruit a sample from which reliable estimates can be drawn. In this study, the use of random household sampling and respondent-driven sampling provided insights into the strengths and challenges of this approach. We present a summary of these findings to inform future research with similar objectives.

Research methodologies are increasingly recognising the agency and significance of non-human entities in shaping socio-environmental dynamics. This presentation will explore the developing field of more-than-human participatory research methods, which challenge traditional anthropocentric approaches by actively involving non-human participants such as plants and animals in research processes. Drawing upon post-humanist principles, more-than-human participatory research methods seek to foster more inclusive dialogues between humans and non-human beings. By integrating the perspectives of non-human participants, more-than-human participatory research methods such as multispecies walking interviews and sensorial ethnographies aim to reveal complex socio-ecological relationships that are often overlooked in conventional research methods. In this presentation, I draw on fieldwork from my ongoing PhD research investigating weeds and horticulture in peri-urban Darwin, Northern Territory, to highlight how more-than-human participatory research methods can foster a deeper appreciation for diverse ways of knowing and being - an important contribution in addressing socio-environmental challenges in the 21st century.

Belief revision is the process of updating one’s beliefs when presented with new evidence, while persuasion aims to change those beliefs. Traditional models of belief revision focus on face-to-face interactions, but with the rise of social media, new models are needed to capture belief revision in text-based online discourse. Here we utilise large language models (LLMs) to develop a model that predicts successful belief revision using features derived from psychological studies.
Our approach leverages LLMs for dimension reduction, using generated ratings to build a random forest classification model that predicts whether a message will result in belief change. Results show that serendipity and willingness to share are the top-ranking features in the model. Our findings provide insights into the characteristics of persuasive messages and demonstrate how LLMs can enhance models based on psychological theory. Given these insights, this work has broader applications in areas like online influence detection and misinformation mitigation, and potential ways to measure the effectiveness of online narratives.

On behalf of the Scanlon Foundation Research Institute, the Civic Opportunities Index aimed to develop local government authority (LGA)-level estimates of civic opportunities available to Australians, adapting methodologies from de Vries et al.’s (2024) U.S. civic opportunities index. This index classifies organisation-level opportunities for events, membership, volunteering, and taking action. The project utilised a combination of data sources: the Australian Charities and Not-for-profits Commission Charity Register, Annual Information Statement Register, and programs register, as well web-scraped data from non-profit organisations’ websites. Organisational opportunities were predicted from the web-scraped corpus using various machine learning approaches (lasso, random forests, and XGboost). This presentation will explore the various methodologies employed in the project to detect and categorise civic activities. The methodologies include traditional web scraping techniques, the use of large language models (LLMs), and human coding. We also address potential shortcomings and limitations in the work, as well as areas of potential improvement.

This presentation will discuss a particular mixed research design which was developed to overcome research ethical concerns in relation to understanding the experience of Indigenous children with neurodisability involved in the criminal justice system in the Northern Territory. Utilising the benefits of both quantitative and qualitative research methods, this presentation will focus on how to address the tension between protecting the privacy and vulnerability of research subject and maintaining the reliability and validity of research findings. This presentation will likely contribute to an going discussion as to how to best design a research methodology in relation to vulnerable people in a justice setting.

This workshop will introduce the GENERATION survey, a new longitudinal study of young people in Australia. Beginning with a national survey of Year 10 students and their schools in 2022, GENERATION provides nationally consistent data on the complexity of youth transitions from school to post-school destinations. GENERATION tracks the interests, hopes and aspirations of young people across Australia in order to shape educational policy for future generations of Australian students. GENERATION is planned to run for a decade, surveying young people annually until they are 25 years of age in 2032. Wave 1 and Wave 2 data is now available to download from the Australian Data Archive.

Data collection for GENERATION began in 2022 with both a Young People’s Survey and a School Context Survey. A representative sample of around 26,000 Year 10 students (around 15 years of age) from over 300 schools participated in Wave 1. This includes over 15,000 fully completed surveys. Over 6,500 young people agreed to be re-contacted after Wave 1, with over 3,500 young people participating in Wave 2 and over 4,000 participating in Wave 3. To accompany the young people’s survey, we also collected data from 259 secondary schools around Australia.

The GENERATION Young People’s Survey provides insights into young people’s career intentions and choices in senior secondary school; educational pathways through school and beyond; engagement of young people as active and informed members of the community; policy and educational factors influencing choices, engagement and outcomes in education and employment; the impact of disadvantage on transitions and pathways; and, the impact of COVID-19 on the cohort as they progress over coming years.

The School Context Survey acted as a supplementary data gathering tool to the Young People’s survey and is linked to school administrative data. The objective of the survey was to collect information about school-based programs and interventions intended to enhance student career pathways, improve student outcomes, and understand how the Pandemic impacted on the school and delivery of programs for the student cohort.

This workshop is designed to provide prospective data users with an overview of the survey design and implementation. It will explore some of the key methodologies used in the GENERATION study. We will provide an overview of accessing and using the data, including an introduction to the documentation, survey weights, longitudinal sample response rates, and data linkages. Key survey items and constructs will be discussed, including standardised measures of student’s career interests, social-emotional skills, and school belonging. We will also highlight some key findings from Wave 1 and Wave 2.

Three members of the GENERATION team will present this session: Professor Ben Edwards (Principal Investigator), Dr Jessica Arnup (Research Fellow), and Kate Doery (Senior Survey Research Officer).

abstract to come

In this free workshop SDAS will teach you some of the neat tricks in Stata to help you do things more efficiently.
Creating commands from the menus
Using the History pane efficiently
Updating commands with clicks
Using logs (results log, cmd log)
Using the graph editor
Resources to help you

PUBLIC LECTURE [FOLLOWED BY Q&A]

There is increasing interest in collaborative research engaging different disciplines/sectors and methods. Are there some proven approaches in planning a large collaborative project involving both qualitative and quantitative research that ensure the qualitative contribute is optimized? 

I begin by describing the appeal of mixed methods. Using examples from both academic journals and student research projects, I discuss studies that mix both qual and quant or just mix different qualitative methods.

Most mixed methods research begins with quantitative data and then moves on to qualitative materials, usually open-ended interviews. I criticize the assumption that this allows us to ‘go deeper’ or more empathetically into social phenomena.

I then outline the limitations of the suggestion that, by using mixed methods, we can reveal the ‘whole picture’ of some social phenomenon. I also draw attention to the naïve positivist assumptions behind much mixed methods research.

Quantitative research must define its variables at the outset in order to measure them reliably. In my view, the beauty of qualitative research is that, mostly using naturalistic data, it allows us to understand how social phenomena are put together rather than to legislate their character at the outset. I conclude by demonstrating a more fruitful division of labour between quant and qual research through which we can gain by mixing methods.

Background: Nurses in Saudi Arabia receive minimal training on caring for patients with dementia. There are cultural views surrounding a dementia diagnosis that it is fate rather than a neurological condition. Therefore, nurses working in hospital settings may not have the appropriate knowledge and skills required to support people with dementia appropriately.
Aims: This study investigated Saudi Arabian hospital nurses’ knowledge of (cognitive expertise), attitudes toward (feelings and behaviors), and perceptions of (thoughts, concerns, and interpretations of behaviors) caring for patients with dementia to inform the development of future training programs.
Design: Six hospital settings in Jeddah, Saudi Arabia, participated in the study. It utilized a sequential explanatory mixed-methods design, involving a survey and diary-interviews.
Methods: Data were collected in Saudi Arabia between March and July 2018. The quantitative data consisted of demographic information and responses to two validated instruments: the Dementia Attitude Scale and Dementia Knowledge Assessment Tool Version 2. The qualitative data comprised of diary entries for ten working days followed by a semi-structured interview. Data were integrated using the Pillar Integration Process.
Results: Registered Nurses (n=710) working in six hospital settings completed a self-administered survey. A self-selecting set of 18 respondents completed a diary-interview. Five themes were identified from the integrated dataset: (1) a lack of knowledge, as well as confidence when caring for patients with dementia, (2) information and resources about dementia care not always available (3) nurses’ attitudes, emotions, and perceived communication difficulties affect their delivery of care for patients with dementia, (4) stigmas, cultural norms, and perceptions associated with a dementia diagnosis can harm nursing care, and (5) organizational factors can harm people with dementia.
Conclusion: Our study showed that the experiences of nurses caring for patients with dementia in Saudi Arabian hospital settings are characterized by significant challenges stemming from insufficient knowledge, limited access to resources, and negative attitudes and perceptions. These findings highlight critical gaps in dementia care provision and underscore the urgent need for targeted interventions to improve the quality of care. By addressing these issues and implementing the recommendations outlined in our study, healthcare organizations can enhance support for both patients with dementia and the nurses who care for them.
Impact: We found that nurses spoke candidly about the emotional toll they experienced when caring for a person with dementia. This emphasizes the urgent need to address the current lack of adequate care for individuals with dementia in Saudi Arabia. Moreover, our study sheds light on the difficulties nurses encounter when providing optimal care, providing a clearer understanding of the key factors affecting patient outcomes. These findings pave the way for enhancements in dementia care, not only in Saudi Arabia but also in other nations confronting similar challenges. By acknowledging and tackling the emotional strain experienced by nurses, targeted interventions can be implemented to improve both the quality of patient care and the well-being of nurses, ultimately leading to better outcomes for individuals with dementia.

What do we learn when we invite others to make, do, and create? How can thread, paint, scissors, and glue help us explore complex, or hard-to-talk about experiences? What do we miss when we ask only with words, and not with action? In this session, inspired by Jackson and Mazzei (2023), we will explore these questions, demonstrating how to enact different arts-based research methods in practices of inquiry to open the process of thought in research. With reference to one line of inquiry as an exemplar – namely, how to examine and/or address anxiety – this session will reveal the complementary value of several arts-based research methods – these include: body mapping; collage making; craftivism; and engaging with found objects. This interactive, creative session will present an overview of arts-based research methods, explaining what they are (and are not) and their purpose; demonstrate how arts-based approaches might be used to examine and/or address anxiety; clarify their benefits, limitations, and ethical considerations; as well as invite conference delegates to consider how they might incorporate arts-based research methods in their scholarship. This session is aimed at experienced and emerging scholars, practitioners, and policymakers.

The ABS Census provides a rich source of socio-demographic information that can be used to inform the geographic allocation of resources and the design of accessible services. The Law and Justice Foundation of NSW promotes the use of such data to support evidence-based decision making across the legal and justice sectors.

Language barriers can have an impact on all forms of interaction, but especially so in the legal context, where the precise interpretation of language can have a significant impact on its meaning and consequences. Population surveys have shown that people for whom English is not their first language are more likely to ignore their legal problems and less likely to seek assistance from a professional, such as a lawyer. There is also relatively poor awareness of public legal assistance services, particularly among more recent arrivals. What local areas have relatively higher counts of people who do not speak English well? What are the most commonly spoken languages in any given area? With easy access to data to unpack these questions, service providers may support improved access to justice amongst non-English speaking communities by providing visibility in specific locations, translating informative materials into the most common languages, and/or considering the availability of interpreters and translators.

This presentation will present a proxy indicator of demand from non-English speakers likely to qualify for public legal assistance services, and demonstrate a dashboard the Foundation has developed which provides a visual representation of the geographic distribution of the indicator, together with counts of the most commonly spoken foreign languages. Aboriginal and Torres Strait Islander languages are considered separately, given the specific cultural requirements of these communities.

This presentation will demonstrate the value of ABS Census data, describe how we overcame its limitations and the challenges we faced in developing a user-friendly tool by which to share the data with the sector. For a sneak peek, the dashboard can be accessed here: https://lawfoundation.net.au/nlas-indicators-and-dashboard/

Introduction
Gambling and related health and social issues among multicultural communities in the Northern Territory (NT), Australia, are frequently noted. Yet, there have been no exclusive studies exploring the views of multicultural people on this topic. Therefore, we conducted this first-ever study to understand the lived experiences of gambling, risks and vulnerabilities related to gambling, and how preventing these risk factors can improve health and social outcomes among multicultural communities in the NT.
Methods
We used a mixed-methods research design in this study to synthesise the findings. In Phase 1 of the study, we conducted an online survey (n=118) with people who gambled on any activity in the last 12 months. We ran basic descriptive and inferential statistics to identify potential relationships between the variables of interest (e.g., predictors of gambling, relationship between gambling risk and health) and used the SPSS software for data analysis.
We conducted in-depth, semi-structured qualitative interviews with those who gambled on any activity in the last 12 months (n=17) and those who were affected by someone else’s gambling (n=8) in Phase 2 of the study. To analyse the interview data, we used an open-coding technique. The themes were identified from the relevant literature and combined with those that emerged from the interview data. Therefore, the coding involved both deductive and inductive approaches to thematic analysis. We used NVivo 10 software to manage the interview data. We obtained ethics approval from two local Human Research Ethics Committees.
Results
We identified positive and significant associations between gambling and having a temporary visa (p <0.001). Further, a positive and significant correlation (p <0.001) was found between gambling participation and self-perceived health. High stress levels; however, were also identified as predictors of gambling (positively and significantly correlated (p <0.001). The variables included on the Problem Gambling Severity Index (PGSI) were also positively and significantly related with having a high risk of gambling (all ps <0.001).
The above findings together with the those identified in the qualitative interviews suggest that financial stress associated with migration forced many participants to work long hours and in multiple jobs, often in positions that paid below the minimum wage. It was dovetailed with challenges to permanent pathways of migration to Australia, a drastic change in lifestyle, a lack of personal and social support, increased stress and anxiety, and a lack of culturally relevant opportunities for recreation and socialisation in the NT.
A desire to overcome such situations was driven by taking risks through gambling. However, these risks put people in negative situations in many cases (e.g., debt, family conflict, poor health). Also, some multicultural communities were prone to such risks more than others.
Some of our study participants discussed ways to regulate gambling more actively to minimise gambling harms in the NT. The usefulness of drawing on the lived experience of gambling and peer support in the gambling recovery process was also discussed.
Conclusion
Our analysis demonstrates that risk-taking is linked to the process of migration and migration is shaped by risk-taking. Migrants are socially, emotionally, and financially vulnerable. Gambling is often considered a pathway to overcome these vulnerabilities.
Gambling is linked to a range of socioeconomic indicators and environmental factors that need to be considered when designing and implementing effective gambling harm reduction and support initiatives for multicultural communities. As noted in the study, some multicultural communities are prone to such risks more than others. Therefore, identifying at-risk communities and developing and delivering tailored interventions may help to achieve tangible outcomes.

Individuals’ interactions with administrative systems (e.g., health, criminal justice, and education systems) produce data about those interactions. In some jurisdictions these administrative data have been aggregated, deidentified, linked at the person level across administrative systems, and made available for research.

Several features of linked administrative data make them ideal for understanding life-course development. First, there are developmentally relevant data at all stages of the life course, from in utero development through to old age and death. Second, the data are longitudinal by design. Administrative records often consist of a timeline of date-stamped events (e.g., births, immunizations, school enrollments, hospitalizations, and monthly earnings), essentially forming a longitudinal record of people's lives as they develop. Third, data linkage across domains permits broad assessments of exposures and outcomes. Fourth, administrative data collections often permit intergenerational investigations through the ability to link between parents and children (e.g., through birth records, legal-parenting-status information, census information, and insurance records). Fifth, administrative collections which capture address or geographic information allow for neighborhood factors (e.g., disadvantage, crime rates, air pollution) to be linked to individual records and assessed in relation to life-course outcomes.

This presentation will give example of how analysis of linked administrative data resources has advanced our understanding of life-course development by utilizing the advantages of these data. Specifically, I will highlight how advances have been made through: (a) understanding small or difficult-to-study populations, (b) using causally-informative designs, (c) identifying early factors predictive of outcomes later in the life-course, and (d) identifying important neighborhood and environmental influences.

I will finish by emphasizing that linked administrative data resources cannot answer all research questions and have a range of limitations, including that (a) the data are not typically collected for research purposes and can be of lower quality (and quality of the data may change over time); (b) the data primarily consist of service contacts, and cases that come into contact with services or seek treatment may form only the tip of the iceberg of those with the condition (e.g., those with more severe problems) and may represent a biased subset; and (c) important factors that rely on observation, self-report, or subjective ratings (e.g., self-control, attachment, parenting quality, loneliness, social cohesion, and measures of the home environment) are typically not captured in administrative systems, so they cannot be the focus of investigations using population-level administrative data.

Community service organisations generate a wealth of publicly available documents that offer researchers invaluable insights often absent from academic literature. These documents provide crucial data on service utilisation patterns, organisational challenges and opportunities, and a rich understanding of how organisations organise and conceptualise their work. Despite their potential, these resources are frequently overlooked in traditional evidence synthesis methods such as systematic reviews. The Law and Justice Foundation of NSW's report, 'Natural Disaster Related Legal Need in Australia: State of the Evidence,' demonstrates the power of incorporating such documents into evidence synthesis. As part of a larger evidence gathering process, this report included systematic retrieval and analysis of annual reports from all Australian community legal centres, legal aid commissions, and Aboriginal and Torres Strait Islander legal centres since the 2019-2020 financial year. Relevant parliamentary inquiries and Royal Commissions were also identified, and submissions made by legal assistance providers and other related community service organisations were also considered. By comprehensively reviewing materials from legal assistance providers—including submissions to Royal Commissions and parliamentary inquiries, annual reports, and major organisational publications—the report generated critical insights about the scale and scope of legal needs following natural disasters in Australia. Most data included in the report had not been incorporated in any previous report on this topic. This presentation explores the advantages and challenges of integrating organisational documents into secondary data analysis and evidence synthesis. The retrieval of these documents provided important service utilisation data for some legal assistance providers that had not been included in other reports and identified barriers and challenges to service provision that are essential for understanding how to best meet natural disaster related legal need. We argue that this approach can significantly enhance our understanding of complex social issues and service delivery landscapes, while acknowledging the methodological considerations researchers must navigate when working with these non-traditional data sources.

Mixed methods-grounded theory (MM-GT) has emerged as a powerful approach, blending qualitative and quantitative data to develop theory rooted in empirical evidence. This presentation explores the use of MM-GT in my PhD study on "How international postgraduate coursework students navigate academic writing expectations in Australian universities", highlighting the rationale behind choosing this method and the unique challenges it presents to doctoral researchers.

The session will cover the entire research process, from aligning the research paradigm with study goals to data collection, analysis and interpretation. Special attention will be given to six critical areas: research paradigm, sampling, data collection instruments, data analysis, integration of findings, and theory construction.

It offers an honest look at the process from research design to completion, discussing the practical aspects, challenges, and necessary adjustments involved. A key focus will be the challenges encountered throughout the study and the strategies employed to address them. By sharing these experiences, I aim to provide insights and tips for researchers considering the use of MM-GT in their own work.

Across the globe, there has been a noticeable increase in weather phenomena such as heatwaves, storms, and intense rains, primarily attributed to changes in climatic patterns. Along with the global scenario, New Zealand's weather patterns are changing, with extreme events occurring more frequently and with greater intensity. New Zealand is affected by a range of weather phenomena, such as tropical cyclones, heavy rainfall, floods, droughts, snowstorms, heatwaves, earthquakes, tsunamis, and the excessive occurrence of El Nino and La Nina. In 2022, the average temperature was 13.15°C, above the average temperature from 1981-2010 by 0.96°C.

The main objectives of this study are to investigate the effect of weather events on hospitalization discharge rates; and to explore the impact of weather variation on heath expenditure in New Zealand.

Author have utilized secondary weather data accumulated from the National Institute of Water and Atmospheric Research (NIWA), as well as hospital discharge rates by ethnic groups and medical expenditure data obtained from Health New Zealand. It has used panel data from 8 major regions which have DHB and NIWA weather stations in all over New Zealand and (2014-2019) have been considered to assess the impact of meteorological occurrences on the health sector. The six-year period includes in this analysis is based on the accessibility of public health data. Panel data regression models (fixed effect and random effect) have been implemented to investigate the association between health variables and meteorological events.

The findings indicate that while the average temperature has an effect on the rate of hospital release, rainfall is inversely correlated with hospitalization. The average temperature separately regressed the discharge rate and the result is positive and significant at 1% significance level. But the admission of individuals to hospitals is not influenced by an increase in the rate of rainfall. The analysis indicates that both the average temperature (1% significance level) and minimum temperature (5% significance level) have had a significant positive impact on the discharge rate.

Both panel regression models (fixed and random) demonstrate that the average temperature has a significant impact on increasing medical cost in New Zealand. It is intended to indicate that individuals are becoming ailing and experiencing a variety of diseases as the average temperature begins to rise. At the end of the day, they are required to undergo medical monitoring. As a result, the authority ought to allocate additional funds to the healthcare sector. In the same way, the total rainfall has a positive association with the medical expense on health care across the nation. The rainfall coefficient has significant impact on medical expenditure in random effect model. According to Hausman test, the result of random effect model is more reliable than the fixed effect model for this study.

The rising costs of healthcare systems are being exacerbated by health issues resulting from extreme weather conditions, particularly in regions with low resources. Gaining insight into the impact of these particular weather conditions on health facilitates the development of more effective health plans and policies. With the increasing frequency of severe weather events, healthcare systems must possess robustness and preparedness to effectively handle the situation. While we may not have a direct role in dealing with climate change and temperature rise, our study can assist health-policy authority in raising awareness among the public about the imminent concerns that climate change poses to the health sector.

Engineering has historically been shaped by socio-historic conditions and traditional masculine norms. Originating in military contexts with violent and mechanistic undertones, it privileges traits often associated with normative masculinity. This deeply ingrained culture not only fosters gender-based marginalization but also gains legitimacy as the status quo. Gender based marginalisation is therefore rife in nearly all engineering environments, both in the subversive idealisation of depoliticization, meritocracy, competition and neo-liberal individualism, in addition to overt sexism and misogyny through symbolic violence, spotlighting, stereotype threat and many more. Consequently, engineering culture delivers benefits to white, heterosexual, able-bodied, middle-class men in the form of a patriarchal dividend. Such a dividend ensures the ongoing dominance and privilege of this group who are invited to view themselves as fearless, enlightened benefactors rather than self-serving recipients of a biased social system. This begs the question of how these privileges are socially reproduced within our tertiary engineering education system. The legitimating and reproduction of this dominance can be conceptualised as hegemonic masculinity. Connell and Messerschmidt (2005) assert that such an undertaking necessitates holistic understandings of gendered hierarchies through the understanding of the mutual conditioning of gender dynamics between the dominant and subordinated groups. This requires nuanced understandings of the constructions of gender negotiations within engineering social institutions, an avenue of research that has often been overlooked in favour of problematising students from underrepresented groups. Such an approach not only illuminates the root causes of hegemonic masculinity within engineering but also provides stakeholders with the scope to dismantle these regimes. Tertiary engineering programs rely heavily on team-based learning, small scale social institutions that exist at the coalface of marginalising social practice and the associated promotion of hegemonic masculine culture.
This qualitative constructivist grounded theory study aims to understand these mechanisms of hegemonic masculine culture reproduction. This theory will be grounded in intensive semi-structured interviews with students and academics at Monash University in addition to videographic observational analysis of in-class teamwork interactions with the use of 360-degree table cameras. This poster presentation will outline the specific methodological challenges and opportunities encountered from adopting these aforementioned methods in the initial sampling phase of a doctoral study. In doing so, motivating the application of constructivist grounded theory approaches in critical studies of the sociology of education and leveraging complementary and innovative methods of qualitative data collection.

Conference cocktail reception - all registrants welcome. Light refreshments will be served.

Abstract
Background: In Sub-Saharan Africa (SSA), the burden of low birth weight (LBW) remains high, leading to considerable short- and long-term consequences for both newborns and mothers. However, limited evidence exists on the geographical distribution and disparities of LBW among adolescent (15-19 years) and non-adolescent women (20-49 years). This study aimed to assess the age-related disparity and geospatial distribution of LBW in SSA.
Methods- This study used Demographic and Health Survey (DHS) data collected between 2010 and 2023 from 33 SSA countries. Concentration curve (CC) and concentration index (CI) were computed to assess age related disparity of LBW. Decomposition analysis using Blinder-Oaxaca approach was conducted to identify factors that contributing to age related disparity of LBW. Spatial autocorrelation and hotspot analysis were performed to assess the spatial distribution of LBW in adolescents and non-adolescents.
Results- A total of 11,144 (6.2%) adolescent and 169,266 (93.8%) non-adolescent women were included in the analysis. LBW was significantly concentrated among adolescents (ECI=-0.096, p<0.001), with a 13.8% prevalence compared to 8.6% in non-adolescents, highlighting a 5.1% absolute difference. The decomposition analysis unveiled that having single children (43.3%), unemployment (7.6%), single marital status (6.9%), and poor wealth status (4.7%) significantly increase the disparity while education (-2.20%) and residence (-1.5%) decrease the disparity between adolescent and non-adolescent women. Spatial autocorrelation and hotspot analysis showed that, LBW was randomly distributed among adolescents but clustered in Mauritania, Mali, Niger, and Chad for non-adolescent women.
Conclusion- This study shows a significant age-related disparity, with LBW disproportionally affecting adolescents. Spatial analysis revealed a random and cluster distribution of LBW among adolescent and non-adolescents respectively. This suggests the necessity of targeted interventions for adolescent women, and policymakers ought to prioritize maternal education, employment opportunities, and financial empowerment for adolescent women as a key intervention to reduce LBW among adolescent women.
Keywords- adolescents, adult women, disparity, LBW, decomposition, geospatial, SSA

The salinity issue is a tremendous difficulty for the southwestern region of Bangladesh due to climate change. Salinity is the measure of the salt concentration that is dissolved in an aquatic environment. Excessive salinity is a major concern in Bangladesh, especially in the southwestern quarter. The rapidly rising sea level caused by global warming is leading to a significant increase in the salt content in the water in the southern region of Bangladesh. Research undertaken in Tala Upazila, Bangladesh, revealed that 94% of inhabitants expressed their willingness to pay for access to clean water, irrespective of their socioeconomic status. Studies have shown that variables such as educational attainment, household income, and the duration of water collection activities positively influence individuals' inclination to pay for water services. In response to rising salinity levels, residents of southwestern Bangladesh are seeking other sources of drinking water. Both the Pond Sand Filter (PSF) and the Filtered Drinking Jar are viable options. Hence, it is imperative to examine the propensity of individuals to alter their source of drinking water. The aim of this study is to investigate the level of willingness to pay (WTP) for different drinking water switching behaviors.
The study includes 1500 households from the south-western region of Bangladesh. In an attempt to investigate user preferences and demand for saline free drinking water, the author has employed a discrete choice experiment methodology. To determine the specific preferences of consumers for any product features, the author employs this approach to obtain the desired outcome. Human willingness to pay (WTP) for drinking water has been determined using a conditional logit model.
According to the study, 30% of persons need 8 liters of water per day and 20.67% need 10. Daily water consumption varies on household size. The study region relies on rainwater for drinking water. Because many use rainwater. People in southwestern saline-prone areas prefer rainwater. The survey found that 45% of southwestern saline-prone residents have two water tanks. Only 17% of responders have 3 tanks. Additionally, 38% have one tank. Southwest saline-prone Bangladeshis want better drinking water. Many refuse to drink better water as water prices rise. 60% of individuals would not choose better drinking water if water prices rose 1 BDT. Improved water-seeking methods boost the desire for safe drinking water. People lose interest in drinking better water when pressured to do so. How well you hunt for water and how acute your emotional pressure are affect learning adaptability. People are willing to pay 245.56 BDT on average, which helps the shift to saline-free drinking water.
Undoubtedly, the residents of the southwestern region of Bangladesh, adversely impacted by elevated salt levels, are encountering a multitude of difficulties arising from this problem. Preservation of primary crops, maternal and newborn mortality, depletion of biodiversity, and other comparable challenges jeopardize their very existence. Primarily, individuals residing in southwestern regions with high salinity levels must endure psychological stress in order to access clean drinking water. Thus, the affected persons are highly motivated to pay for access to an improved drinking water supply. The salt-prone southwest region necessitates marketing efforts to improve the quality of drinking water supplies. Purifying drinking water requires technical assistance from the municipal authorities. Non-governmental organizations can coordinate initiatives addressing the limitations of rainwater extracting. The municipal governing body has the authority to grant permission to the water distribution authority to disseminate information regarding the enhanced water sources.

This study investigates the political economy of COVID-19 vaccine access in Southeast Asia, focusing on the interplay of institutional, ideational, and actor interests in the trade and health sectors during the pandemic. It explores how these factors interact and impact equitable vaccine access, using COVID-19 as a case study.

Grounded in constructivist epistemology, this research aims to understand the underlying structures driving vaccine access in Southeast Asia. Constructivist epistemology is particularly valuable for this study due to its focus on the social, political and cultural factors underlying vaccine access. The methodology primarily employs qualitative methods, supplemented by social network analysis to highlight discrepancies in vaccine availability across the region.

The research adopts an international political economy and health governance approach, with national, regional, and global levels of analysis. This approach is well-suited for answering the research questions, as it examines multi-level governance and the political economic conditions influencing vaccine access. The study considers interactions among Southeast Asian institutions and political actors operating across different territorial boundaries including with international organisations and with varying governance resources.

The research design employs a multimethod approach, combining document analysis and comparative case studies through interviews. It is divided into two phases:

Phase 1: Document Analysis
This phase analyzes policy and regulatory documents to understand how intellectual property, trade, and health issues are framed by governments and intergovernmental organizations. Using a thematic analysis approach, I will categorize recurring themes and patterns within the data. Using a scoping exercise of existing literature, I will map the levels of vaccine access in countries across the region at critical COVID-19 pandemic junctures and delve into the types of vaccines acquired by each Southeast Asian country. This review will help delineate universal barriers to vaccine access shared among all Southeast Asian countries, as well as those specific to the region.

Phase 2: Comparative Case Study
This phase involves interviews with key stakeholders, including former and current policymakers, experts, diplomats, business actors, and civil society members. The interviews aim to identify regional challenges and country-specific barriers to vaccine access in the context of international trade and health policy. By eliciting empirical data, the study seeks to understand the complexities of vaccine access and the dynamics of political economic conditions that contribute to it.

By elucidating these complexities, the research aims to contribute to health equity in Southeast Asia and shed light on the dynamics of equitable vaccine access. The findings have the potential to provide insights into the political and economic factors that influence vaccine distribution and offer recommendations for improving access in the region.

Navigating the transition from school to employment or further education is a critical juncture that can shape a young person's long-term economic trajectory. However, many Australian young adults face significant disadvantages and barriers that limit their opportunities, which can vary drastically across different communities. Some areas struggle with elevated poverty rates and high disengagement from school and training, while others lack sufficient local employment prospects aligned with young people's skills and interests. Without granular, place-based data, it is difficult for policymakers, community organizations, education providers, and other stakeholders to fully understand these nuanced challenges and make informed decisions to support positive youth transitions.

YouthView is an innovative online platform that combines rich Australia-wide datasets on youth disadvantage indicators like poverty, NEET (not in employment, education or training) status, and unemployment with detailed employment data, skills demand, and job vacancy information at the regional level. This powerful tool communicates important research findings in two ways: a guided storytelling mode that highlights key insights through interactive maps and visualizations, and an open-ended exploratory dashboard.

The storytelling mode illuminates critical spatial patterns and socioeconomic disparities based on factors like geographic region, remoteness, and proximity to capital cities. However, YouthView goes beyond just presenting findings. The interactive dashboard allows users to filter, disaggregate, and visualize youth disadvantage metrics alongside employment, skills needs, and business data for any area of interest across Australia. Stakeholders can overlay related datasets to analyze complex relationships between disadvantage factors, labor market dynamics, and economic opportunities.

By integrating data storytelling with self-directed exploration and visualization of granular community-level data, YouthView empowers data-driven decision-making and planning. It can guide the development of targeted policy responses, interventions, and resource allocation to improve youth economic outcomes and expand opportunities where needed most.

Introduction
Effective chronic disease prevention initiatives (policies, programs, and practices) need to be implemented and sustained in key settings to achieve long-term public health impacts. While progress has been made in understanding how to implement initiatives, their sustainment, especially after external support ends, remains understudied. Systematic reviews indicated that only around 20% of initiatives sustain after the initial implementation period (1). However, some programs do continue without ongoing support. This approach, termed ‘self-sustaining’, has been defined as: “when implementation of the EBI by an organization (e.g., hospital, clinic, school) is expected to continue (sustain) in the absence of external (agency) support”(2).

Sustaining initiatives offers numerous advantages including efficient resource allocation within no ongoing costs, maximising benefits for the target settings, and achieving a broader public health impact. With many initiatives implemented with short-term, non-recurrent funding, understanding how to sustain after the funding period is critical. This requires understanding the conditions that lead to self-sustainment, information that can be translated to the program design phase to facilitate long-term sustainment.

Novel methods are warranted to advance this evidence base, given the lengthy timelines required for longitudinal sustainment research. One such method - increasingly recognised for its ability to explore program mediators while accounting for the variability present in complex settings - is Qualitative Comparative Analysis (QCA) (3). QCA allows for the configuration of necessary and/or sufficient conditions that lead to an outcome of interest, offering insights into program sustainment. This study aims to identify the conditions associated with the self-sustainment of chronic disease prevention initiatives, specifically focusing on those in the school setting (i.e., school-based programs which have sustained after the cessation of external support).

Methods
This study will use a crisp-set QCA (csQCA) methodology, as outlined by Kane et al (3). Comprehensive case studies of self-sustaining initiatives will be developed using various data sources, including research staff insights, systematic reviews, published data, grey literature and publicly available websites. Definitions and thresholds for both cases and conditions of interest will be ascertained through an in-depth literature review; expert consultations; and ongoing discourse within the research team inclusive of sustainability experts, implementation scientists, school-setting practitioners, and a qualitative research expert trained in QCA.

Conclusion:
This study will be the first to rigorously explore the conditions that are necessary and/or sufficient for initiatives to self-sustain in the school setting. Specifically, this will isolate the key factors that operationalise self-sustainment which, translated into practice, may support the design of programs with the best chance to sustain without ongoing support. This is particularly valuable for settings with limited resources and/or multiple competing interests, such as schools. Additionally, this study will contribute to the growing public health research applying QCA methods and set precedence for future applications of QCA.

References
1. Wiltsey Stirman S, Kimberly, John., Cook, Natasha., Calloway, Amber., Castro, Frank., and, Charns M. The sustainability of new programs and innovations: a review of the empirical literature
and recommendations for future research. Implementation Science. 2012;7(17).
2. Wolfenden L, Shoesmith A, Hall A, Bauman A, Nathan N. An initial typology of approaches used by policy and practice agencies to achieve sustained implementation of interventions to improve health. Implement Sci Commun. 2024;5(1):21.
3. Kane H, Lewis MA, Williams PA, Kahwati LC. Using qualitative comparative analysis to understand and quantify translation and implementation. Transl Behav Med. 2014;4(2):201-8.

Singing is an expressive and versatile tool that can be used in research design, data collection, data analysis and the presentation of findings. In this short video, viewers will be introduced to a grounded theory study on the facilitator processes in group singing. The video will showcase how songwriting, group singing, conversational song, and sung memos were integrated into key phases of the grounded theory design. This melodious presentation aims to inspire fellow researchers to harmonise alternative expressive modes, such as singing, with established research methods.

Social media platforms such as Twitter/X and Reddit are increasingly important for political communication: opinion leaders and influencers use social media for one-to-many communication, but these spaces also enable ordinary citizens to form opinions by engaging in one-to-one discussion about social and political issues. It is important to understand how social media are facilitating or impeding political deliberation, a process whereby individuals with differing perspectives and opinions engage in discussion, potentially revising their opinions upon hearing the arguments of others.

In this presentation, I outline research into political deliberation on Twitter, conducted as part of the Volkswagen Foundation-funded “Bots Building Bridges (3B)” project. We first used a set of debate- and election-related hashtags to undertake a collection of tweets authored during the first 2020 US presidential debate. We then used the v2 Twitter API to collect the wider Twitter conversations that these tweets were part of, so our final dataset also included debate-related tweets that did not feature the target hashtags. The resulting dataset consists of over 13K reply tree network with a “conversation starter” tweet as the root node and all the subsequent replies and replies-to-replies.

I then present some preliminary findings regarding the deliberative nature of Twitter activity during the first debate, focusing on two types of analysis. First we construct a measure of deliberation involving the depth (proxy for argumentation) and breadth (proxy for representation) of reply tree networks. Second, we construct a random sample of root-to-leaf reply chains extracted from the reply trees, with the chains then manually coded for agreement, conflict, and incivility. An overall aim is to understand how deliberation trajectories vary with topics of discussion and the partisanship of the discussion partners.

As nonprobability samples become more and more prevalent throughout survey research, it is important to understand the impacts of the choice of sampling frame on the many aspects of data quality. Satisficing is one such aspect that is widely researched, but less so in comparative studies of probability and nonprobability sampling frames.
The Australian Comparative Study of Survey Methods (ACSSM), fielded in December 2022, systematically trialled eight sampling frames and survey modes: web mode using a probability-based online panel, Life in Australia™ (n = 585), a video interviewing arm recruited from Life in Australia™ (n = 600), a cell phone RDD CATI stream (n = 803), RDD text-to-web (n = 600), and four nonprobability access panels (n = between 853 and 892 per panel). The ACSSM follows an earlier Australian comparative study fielded in 2015, which compared various probability and nonprobability modes (Lavrakas et al. 2022), and its replication on Life in Australia™ in 2017 (Kaczmirek et al. 2019).
Here we investigate satisficing (sometimes known as careless or insufficient effort responding), assessing the degree of prevalence through various metrics. This analysis includes comparisons between the sampling frames, with particular attention to the differences between the comparable online probability and nonprobability frames. We use several data quality indicators that are associated with satisficing and careless response—straightlining, speeding, logic checks, outlier analysis, item nonresponse, and midpoint selections, along with a composite metric. Results show differences between probability and nonprobability frames, with a higher proportion of satisficing consistently observed among nonprobability online panels. Further, we explore the bias of survey estimates after excluding cases who are flagged for satisficing and observe a significant bias reduction among nonprobability online panels. Our research indicates that in addition to the well-known errors of representation associated with nonprobability online panels with respect to coverage and nonresponse errors (Baker et al. 2010), there is also a higher level of measurement error.

Today’s digital information ecosystem is complex. There are unprecedented amounts of personal information collected and shared for different purposes. It is unreasonable to expect Australians to engage meaningfully with notices from the large number of entities seeking to handle their personal information. Industry has been vocal about having to shoulder the cost of privacy regulation. But what is it costing Australian consumers?

The Consumer Policy Research Centre devised and undertook a unique social research study using an online ‘privacy sweep’ methodology across a 24 hour period from 15-16 May 2024. The aim of our research was to pilot a methodology designed to quantify the burden of time and effort placed on Australian consumers in what it takes to manage their online privacy in 2024 in Australia. This research contributes to CPRC's ongoing recommendations to the Federal Government to take a series of measures to reform the Privacy Act.

A group of citizen scientists participated in a 24 hour 'privacy sweep', which involved two tasks and a series of evaluative metrics to be undertaken for each new website or app visited within the 24 hour period. Task 1 required participants to locate and adjust the privacy settings of every website or app they engaged with, whereby the survey timed how long each action took. Task 2 required participants to use an automated word counter to conduct a word count of the privacy and cookie policies of the apps and websites they were using, logging this information into an online survey. Ease of performing each task, and understanding the policies was measured using a series of 5 point Likert scale metrics.

In total, 98 records were collected from a group of nine citizen scientists from consumer organisations and universities including CHOICE, Reset, the University of Melbourne, and CPRC; eight were based in Australia and one based in Europe. We discovered that on 45% of occasions, participants found it difficult to locate and adjust privacy settings. We also found that each site or app required anywhere from two to ten minutes of time for Australian participants to locate and adjust privacy settings. This was in stark contrast to the participant based in Europe who spent on average only 3.1 seconds per website to manage privacy settings. The sweep also revealed that the average word count of a privacy policy was 13,323 words, equating to an average reading time of 56 minutes per policy, and when aggregated over the 24 hour period, would take 14 hours on average to read all policies encountered.

Our research highlights a mismatch between community expectations and data practices. While businesses can quantify regulatory costs, the time and effort required by Australian consumers to manage their digital privacy often goes unaccounted for.

This methodology provided a rapid turnaround study that quantified the time impost placed on consumers in 2024 in Australia to manage their own privacy. While this pilot test was modest in size, it could easily be expanded in scope and tracked over time to determine if businesses practices are changing as a result of government pressure or changes to legislation that we hope to see very soon.

Surveys are an important tool in social studies, and most surveys require respondents to rate items on a Likert scale. In this sense, the data collected are often ordinal in nature. This very nature poses challenges to data analysis, as many statistical techniques become inappropriate. In this presentation, a parsimonious mixture distribution, called the combination of uniform and binomial (CUB), which is specifically built for ordinal data, will be revisited. Under CUB, each response is assumed to originate from either the respondent's uncertainty or the actual feeling towards the survey item. In other words, the CUB model can account for respondents' hesitation or indecisiveness towards the survey question, making it a powerful tool to capture the extra variabilities inherent in the responses.

Since most surveys contain more than one question, the data collected are multivariate in nature, and the associations between different survey items are usually of considerable interest. An extension of the univariate CUB model to the bivariate case will be introduced. Most of the previous attempts employed the method of copula, which makes interpretation difficult. In the opposite, our proposed method bypasses the use of copula and allows the associations between the unobserved uncertainty and feeling components of the responses to be estimated. This distinctive feature makes our proposed model more interpretable compared to copula-based ones. In addition, the model parameters can be shown to be identifiable, making the model statistically valid.

Such a bivariate CUB model can serve as a tool for analysing survey data in social sciences and other disciplines. This presentation will describe the underlying logic and both theoretical and practical aspects of the proposed model, and will demonstrate its application through a real-word example.

This talk explores shifts in the party structure within Australian parliamentary networks from 1947 to 2019 using graph theory, focusing on the career backgrounds of Members of Parliament (MPs). We analyzed two sets of graphs: one representing MPs and the other capturing their career backgrounds. For the MP graphs, we compute key metrics such as average maximal flow and transitivity to investigate both the local and global connectivity. We then generate two sets of random graphs for statistical comparison.

In the career background graphs, we utilize betweenness centrality to identify influential nodes and detect bouquet structures. This comprehensive analysis reveals structural changes in political networks over time, providing deeper insights into the evolving dynamics of party structures.

“Exploring the linkages between rental repairs rights and energy inefficient homes” is an action project that examines how these inefficiencies increase financial burdens through higher utility bills and how renters experience disempowerment while navigating the legal system. The project is a cross-disciplinary collaboration between Anika Legal, the Consumer Action Law Centre, and the Consumer Policy Research Centre, combining legal, financial, and policy expertise.

Energy inefficiency in rental properties often affects vulnerable renters who face disproportionate financial stress. Renters may struggle to address these issues through the legal system, as the complexity and inaccessibility of legal processes can leave them feeling disempowered. Renters’ capacity to improve their quality of life is further obstructed by the power imbalance inherent within their relationship with their landlord. This project aims to capture the full extent of these experiences by examining both the financial impact and the renters’ interactions with the legal system as they attempt to enforce their rights.

The research methodology emphasises data collection across the service delivery lifecycle, beginning with the cases that come through Anika Legal, where renters report issues of energy inefficiency as linked to disrepair within the rental home. Additional data from the Consumer Action Law Centre captures the financial strain renters experience due to elevated energy costs. The culmination of the research is a series of in-depth interviews with renters, aimed at understanding their personal stories, particularly the sense of disempowerment that arises when dealing with legal barriers.

By integrating quantitative data with these personal narratives, the project highlights the systemic issues that make it difficult for renters to navigate legal and financial pathways. The collaborative nature of this partnership allows the project to address these issues from multiple angles, ensuring a more comprehensive understanding of the challenges renters face.

This presentation will explore key findings from the project, including the financial and legal difficulties renters encounter, and will contribute to discussions about strengthening consumer protections in the rental market. By sharing our research methods and insights at the Social Science Methodology Conference, we hope to encourage dialogue around how interdisciplinary collaborations through action research can help address disempowerment and improve consumer outcomes in other sectors as well.

In economics and finance, many decisions are based on uncertain outcomes. Published economic data, such as the unemployment rate or GDP growth, are based on estimates that have confidence bands. The uncertainty around the estimates is even larger for forecasts or projections. Most economic data, actual data, and forecasts are published as point estimates that lead people to ignore the uncertainty around the estimates. Ignoring these uncertainties leads to worse decisions.

Our paper explores how to convey probabilistic information in economics and finance to laypersons. We conducted a survey experiment within ANUpoll, a quarterly survey representative of the Australian population. ANUpoll is based on Life in Australia, a probability-based panel run by the Social Research Centre.

We randomly showed respondents one of four plots: a kernel density plot, a histogram, a quantile dot plot with 20 dots, and a quantile dot plot with 50 dots. We then asked each of the treatment groups the same four questions to gauge their understanding of the information conveyed by the plots. Respondents were told what was shown but received no further instruction regarding the interpretation. Each plot summarised the same probability distribution of forecasts of the Australian unemployment rate. We took these forecasts from actual figures published by the Reserve Bank of Australia. When shown the randomly assigned plot for the first time, respondents were asked to provide their best forecast of the unemployment rate one year out. We repeatedly reminded respondents to base their estimates on the shown plot.

Our experiment was included in the online questionnaire for January 2024. A good-sized sample of about 4,000 respondents completed that questionnaire. In addition to the data from the experiment, we were able to access rich data from the current and earlier waves of ANUpoll. This enabled us to examine how well the four plot types performed and whether this varied by sociodemographic and economic variables. We also found that incorporating figures into the online questionnaire was straightforward and worked quite well. Online surveys should consider doing this more often.

Introduction
As social media usage continues to proliferate, platforms have become hotspots for recruitment into modern slavery. While research has predominately focused on recruitment into forced sexual exploitation, some efforts have been made to analyse recruitment into forced labour by utilising computational methods to gather and analyse online communications related to modern slavery activities (Williams, Burnap, & Sloan, 2017), albeit with limited attention to social media practices. Filling this gap is important to combating modern slavery as forced labour in the private economy makes up the largest share of global modern slavery (35%) and the majority of all forced labour cases (63%) (International Labour Organization, Walk Free & International Organization for Migration, 2022). Combating forced labour on social media is a significant challenge for stakeholders seeking to remove high-risk job advertisements, particularly those targeting prospective migrant workers, as traffickers embed content within visual and audio materials. This covert approach allows them to avoid detection by moderation protocols and obscures their criminal purpose from potential victims (de Vries & Radford, 2021).
This study bridges this gap by employing a multi-modal approach that integrates visual, auditory, and textual analysis. By examining social media data, this study aims to uncover and understand the complexity of how risks of modern slavery can present within recruitment and employment-related posts.

Methodology
Social media posts from six leading social media platforms (e.g., Tik Tok) were collected including textual, audio and visual data. These posts were related to job advertisements in the domestic work and construction sectors in the Middle East and North Africa, with a particular focus on countries within the Gulf Cooperation Council. Data preprocessing includes removing URLs and irrelevant characters to ensure uniformity. Audio data and video on-screen text were converted into text. Non-English posts were translated into English to maintain consistency in the analysis.
Natural Language Processing was employed to analyse textual data and detect risk markers of modern slavery, such as: 1) Topic modeling was applied to uncover prevalent themes related to exploitation and slavery within the collected data; 2) Pattern recognition algorithms were developed to identify specific linguistic patterns and keywords associated with potential modern slavery activities, such as references to coercion, restricted freedom, or abusive working conditions.; and 3) Co-word analysis was utilised as a visual technique to detect the co-occurrence of specific terms and concepts, facilitating the identification of clusters and patterns that can denote risk markers of modern slavery.

Conclusions
Our research has revealed several important findings. Findings show that potential traffickers’ social media posts format and structure tend to have more hashtags and limited captions, more text and descriptions within images or videos, and often contained multiple jobs in one advertisement post. We posit that these characteristics serve to appeal to as many prospective migrants as possible, using strategies to be viewed by large audiences on social media platforms, and sharing messages designed to entice and deceive. This research also highlights the challenges in analysing multi-modal data to determine whether ‘risk markers’ are present within social media posts where there is limited contextual data based on the sector, country, or language.
This research significantly advances the fields of digital criminology and social media analytics by developing a novel multi-modal analytics approach to uncover and understand the complexity of modern slavery risks within recruitment social media posts. The findings can aid law enforcement agencies, NGOs, and social media platforms in the detection and mitigation of modern slavery risks on social media, contributing to global efforts in combating this critical issue.

I present a novel algorithmic impact assessment methodology to perform systematic audits of online Australian consumer insurance markets and evaluate whether online systems being used provide fair outcomes for people regardless of their ethnic, racial, or gender profile. Its interdisciplinary design brings together qualitative critical data and internet studies approaches with quantitative computational and automated approaches for systematic evaluation of bias and discrimination. The methodology is deployed across 10 insurance platforms comprised of direct to consumer, retail, and comparison websites. This collaborative project brings together experts from academia and consumer advocacy organisations to forge an applied research agenda grounded in public transparency, accountability, and human rights.

Insurers rely on complex algorithms to guide their decision-making capacity, set premiums, and personalise insurance products. Advances in data-driven and AI systems are significantly transforming the emerging insurance technology (insurtech) sector. Such technological advances bring the promise of greater optimisation, as well as great risks for the reproduction of bias and reinforcement of discrimination. The risk that these systems may result in forms of disparate impact is one of the most significant threats to antidiscrimination regimes posed by this transformation, both within Australia and globally.

This methodological framework investigates how data-driven and AI systems impact the provision of consumer car insurance via pricing algorithms which may produce unfair outcomes for particular subsets of society by engaging in proxy and price discrimination. It examines whether consumers are quoted more for general car insurance based on particular characteristics. It aims to identify if protected characteristics correlate with higher prices in online insurance markets. This research will produce the first empirical evidence of the impacts of data-driven and automated decision-making systems within the consumer-facing insurtech industry in Australia.

The research deploys 3 methodological strands:

1: A detailed scoping review systematically identifies, descriptively maps and explains the body of evidence of whether the use of data and automated decision-making systems leads to consumer outcomes that vary on the basis of protected characteristics.

2: Qualitative digital forensics investigation thoroughly examines consumer facing platforms of major online insurance providers in Australia. This method critically investigates the environment of expected use, observes promotional nudges, documents consumer data collection techniques and automated sequencing for data production, examines legal, policy, and financial frameworks to understand data relationality–between insurer and consumer–produced by sociotechnical data flows and that may produce harmful social and economic effects.

3: A quantitative sock puppet audit technique is designed as an automated process to conduct mystery shops of online insurance platforms providing consumer comprehensive car insurance. The sock puppet mimics realistic user behaviour in place of people and enables more carefully controlled testing than would be possible with human testers in real-world settings. It also avoids ethical implications of human data collection and the inclusion of vulnerable populations in research.

Our sock-puppet uses synthetic data (proxy personas) associated with specific demographic profiles representing 10 different ethnic groups in Australia that vary by name, location, and gender. 720 personas have been designed in collaboration with spatial demographic experts to ensure rigor and relevance. Name and location act as proxy for the ethnicity, race, and gender, while specific postcodes are selected to represent areas with different ethnic minority/majority and Indigenous compositions. All variables are controlled for to provide a statistically reliable frame of analysis, and to minimise any confounding effects from unmeasured variables. We deploy the sock-puppet to examine whether people with the same name receive different quotes when living in different areas, and whether people with different names receive different quotes from others living in the same area.

Literature on the alt-right’s social media presence in the mid-2010s asserts the alt-right’s implication in other significant online phenomena of the period. Researchers have argued that social media platforms are opportune spaces for extremism to occur, highlighting the co-occurrence on the Reddit social media news platform of the misogynistic online harassment campaign Gamergate, the MAGA political movement popularised by Donald Trump, and the alt-right. But the implication of MAGA and Gamergate in the alt-right remains more asserted than empirically demonstrated to date. Therefore, this research asks: to what extent did alt-right activity relate to the activity of Gamergate and MAGA on Reddit? This analysis uses social network analysis (SNA) to identify the alt-right and its activity on a number of subreddits on Reddit, before exploring whether the alt-right is so closely related to MAGA and Gamergate that their collective constituencies of millions of Redditors can be generalised into the alt-right.

This research demonstrates a relational methodological framework to examining the overlaps and distinctions between reputationally and circumstantially adjacent phenomena on a large-scale platform like Reddit. Through applying SNA to large-scale Reddit trace data, it finds that alt-right Redditors constitute a narrow and largely unique constituency on Reddit. Alt-right Redditors, as identified among subreddits like /r/altright, see the usefulness of the presence and momentum of MAGA and Gamergate as an opportunity to ‘redpill’ potential ideologues into the alt-right’s Anti-Semitic, white nationalist identity through drawing on similar grievances to their ideology. However, Gamergate and MAGA Redditors in /r/KotakuInAction and /r/The_Donald largely do not engage in alt-right activity among /r/altright and its counterparts. These phenomena remain qualitatively distinct in ideology and activity, including through expressing disinterest or repellence at the extremism of the alt-right. Notably, alt-right Redditors also resent how third-parties conflate the alt-right with Gamergate or MAGA due to the perceived ideological impurity of the latter phenomena. This analysis finds that the alt-right was only loosely associated with these larger adjacent phenomena during this mid-2010s period. Its methodology and findings justify the necessity of examining the specific relationship between phenomena to avoid presumptive generalisations of social media users that may belie significant differences of ideology and behaviour.

The Australian Comparative Study of Survey Methods (ACSSM) systematically compared the accuracy of estimates achieved through traditional, contemporary and evolving survey methods. These included random digit dialling (RDD) computer-assisted telephone interviewing (CATI), the nation’s first and only probability-based online panel (Life in Australia™), a video-assisted live interviewing arm recruited from Life in Australia™, RDD SMS push-to-web, and four non-probability online panels. The study was fielded in December 2022.

We compare the performance of Life in Australia™ to the other survey methods included in the study with respect to an extensive list of high-quality external benchmarks. The broad range of modes trialled help to situate online panel performance with respect to both self- and interviewer-administered modes and probability and non-probability frames. This study contributes to the multinational evidence base on the performance of online probability-based panels in comparison to non-probability methods.

This presentation provides an overview of the methodology employed for the comparative study, key findings and implications for general population survey research. We also share additional analysis and innovations inspired by the ACSSM.

The idea of data sovereignty has gained traction over the last decade. Data as a new resource are increasingly vital for decision-making in businesses, governments, and societies. Proponents of data sovereignty resist exploitation of personal data especially by state and corporate power. This study aims to map and explore issues around ‘data sovereignty’ raised by non-state and non-for-profit organisations on the internet.

This study uses quantitative analysis of text content produced by organisations on the web to explore the relationship between status of organisations and issues they have raised. Combining field theory and social network analysis as co-analytical tools, social positions of organisations are identified based on their differential possession of capital manifested both offline and online. Offline data on the number of staff, the presence of international office and consultative status in the UN are used to estimate economic capital of organisations, while the reach of issues is used to estimate the level of cosmopolitan capital. Economic and cosmopolitan capital are jointly used to estimate the ‘offline status’ of organisation. Online data such as organisations’ popularity and activity in online networks are used to determine the ‘online status’ of organisations in the field.

This study uses correspondence analysis to investigate association between the status of organisations and the issues they are focused on. The preliminary finding suggests that some of the prominent concerns of actors with high offline status, such as the issue of ‘rights’, are also focused on by more peripheral actors in the online field. Thus, mapping data sovereignty issues using a combination of field theory and network analysis can help reveal shared issues among different organisations across different contexts, allowing insight into the emergence of contested data sovereignty issues in the field.

Nonresponse is a critical issue for data quality in panel surveys. Many researchers have demonstrated the potential of machine learning models to predict nonresponse, which would then allow survey managers to pre-emptively intervene with low-propensity participants. Typically, modelers fit their machine learning models to the panel data that has accumulated several waves and report which algorithm and variables yielded the best predictive results. However, these studies do not tell a manager of a yet-to-commence panel survey which technique is best for their own context (e.g., annual vs. quarterly waves, household vs. individual sampling). Studies have shown mixed results regarding the performance of nonresponse prediction in different panel contexts. In addition, there is considerable variation in which prediction technique (e.g., algorithm and variables) performs best across survey settings. It is thus unclear under which conditions predictive models successfully identify nonresponders and which techniques are best suited to which contexts.

To address the question of cross-panel generalizability, we compare machine learning-based nonresponse prediction across five panel surveys of the general German population: the Socio-Economic Panel (SOEP), the German Internet Panel (GIP), the GESIS Panel, the Mannheim Corona Stud (MCS), and the Family Demographic Panel (FREDA). We evaluate how differences in the design of the surveys and differences in the sample composition (e.g., average sample age and income) impact the characteristics of the best-performing machine learning model (e.g., the best algorithm, accuracy scores, and the most predictive variables). We compare which (types of) variables and algorithms are the most predictive across these contexts. We also evaluate how well techniques from one survey transfer to a different survey context. Our analysis shows the extent to which practitioners can expect the modeling techniques of one survey to generalize to their own context and the factors which might inhibit generalizability.

Increasing cost-of-living and energy prices are nudging Australians towards products that save energy and are cost-effective. In addition, climate change, sustainability and the impact of consumerism is also top of mind for many Australians, elevating the importance of environmental and social responsibility motivations and the circular economy when purchasing across various sectors.

Given these motivators of cost, and environmental consciousness, in crowded marketplaces, businesses are leveraging the use of energy efficiency and environmentally-adjacent terms to market their products, some of which can be considered "greenwashing".

Common terms used in marketing materials include “efficient”, “clean”, “green” and “sustainable”, and there are growing concerns that many ads using green terms are vague, unhelpful and unclear. These words give companies a “green halo” without helping customers to genuinely compare their options.

CPRC is part way through a multi-stage research study, funded by a grant provided by Energy Consumers Australia. The study seeks to investigate and audit the information present at the point of sale of large, energy-intensive home appliances, and provide an in-depth understanding of the consumer decision-making process when faced with a plethora of information and marketing at the point of display/purchase. We have appointed an experienced Reference Committee comprising consumer and energy policy representatives from consumer organisations and government, including CHOICE, Justice and Equity Centre, Care Financial Counselling, and the Australian Energy Regulator. Our Reference Group is assisting with content of the research as we progress through the stages.

Research stages include:
A. Mystery shopping and auditing consumer information in large appliance retail environments (August 2024). We are assessing the information and claims made about energy/environmental factors in online and offline retail settings to determine what consumers are faced with at the point of purchase. Appliance types were selected in collaboration with CHOICE’s Product Category Manager (Household), and comprised six of the more energy-intensive home appliance types: fridge, washing machine, clothes dryer, electric oven, split system air-conditioner, and electric heater.

B. Qualitative exploration of consumer views on what information they value, need and are influenced by at the point of purchase (October 2024). A series of 8 focus groups across four states with renters and home owners will explore consumer responses, attitudes, perceptions, expectations and experiences of energy-related information – overall and used in the context of large home appliances – with recent purchasers and purchase intenders. Broader issues such as barriers to electrification and energy bill stress will be explored also, highlighting differences between home ownership status and life stage.

C. Quantitative measurement of the impact of various pieces of consumer information in large appliance purchase decisions (early 2025). This stage will employ a discrete choice experiment which draws upon all information collected in the first two stages. We will be surveying n=1,500 Australians in the market for a large appliance, in a discrete choice experiment to derive the hierarchy of decision-making factors and information usage when consumers purchase large home appliances.

At the end of this study, we will understand if consumers have the information they need to assess the energy efficiency and environmental impact of home appliances, and what may need to change in the regulatory environment to ensure consumers can be confident and supported to make energy-efficient purchase decisions on large, energy-intensive home appliances.

An ACSPRI Executive Committee Member will announce the ACSPRI Fellowship 2024/2025 winner, along with the winner of the best Poster/Short video competition. The conference major sponsors will be recognised. The closing plenary will be introduced.

Social network analysis has become a key tool in digital communication research, and has flourished especially in social media studies using Twitter data. However, it struggles in analysing activity patterns on platforms that provide fewer data points on interactions between accounts (e.g. Facebook or Instagram), and in exploring the interconnections between multiple activity practices that are interwoven with each other. This keynote introduces the new approach of practice mapping, which advances beyond the network analysis and visualisation of direct interactions between accounts and instead uses vector embeddings of networked actions and interactions to map the commonalities and disjunctures in the practices of social media users. In particular, this innovative methodological framework has the potential to incorporate multiple distinct modes of activity and interactivity into a single practice map, can be further enriched with account-level attributes such as information gleaned from textual analysis, profile information, available demographic details, and other features, and can be applied even to a cross-platform analysis of communicative patterns and practices. Drawing on a case study of public posting activity on Facebook during the Voice to Parliament referendum campaign, this keynote outlines the practice mapping approach and demonstrates the insights it can produce.

Introduction: Schools have been identified as a key setting to deliver public health programs. To fully understand the value of school-based public health programs, it is essential to explore not only the primary health impacts but also any additional benefits they may offer for children. Additionally, exploring how these programs affect different subgroups of children can help to address health inequities. This information is attainable through qualitative research methods, however school-based research rarely includes children. We used novel qualitative methods with students, supplemented with interviews of teachers, to explore the impacts of a daily sport uniform policy aimed to improve student physical activity.

Methods: We purposively sampled schools from urban and rural areas in NSW, Australia that had implemented a daily sport uniform policy. Consenting schools facilitated recruitment of teachers and student participants. Semi-structured focus groups of students and interviews of teachers were conducted during school break time. For student focus groups, we used creative, age-appropriate techniques such as visual and written responses on post-it notes and physical movement to ensure student comfort while enhancing their engagement and contribution. Inductive thematic analysis was then used for both data sets (students and teachers), with triangulation of findings across data sets.

Results: Focus groups and interviews involved 13 students and two teachers across different schools. Students were highly engaged with the focus group activities, eliciting rich verbal and written/visual data for analysis. Analysis revealed several key findings: sport uniforms not only enhanced student physical activity during and outside schools hours (as intended), but they also had numerous other positive impacts on student wellbeing including reduced stress, and increased comfort. The daily sport uniform was especially important for girls, allowing them to move freely and feel more confident every day of the school week.

Conclusion: The use of innovative and pragmatic qualitative methods in this study allowed for an in-depth exploration of the impacts of a daily sport uniform policy, capturing both anticipated and unexpected effects on students and teachers. The method we used ensured high engagement and comfort among student participants, yielding rich, multi-dimensional data. The triangulation of findings from both students and teachers strengthened the robustness of the results. These methodological approaches underscore the value of qualitative research in school settings, particularly when working with children, and demonstrate the importance of using intentional qualitative methods to fully capture the diverse impacts of public health interventions.

Indonesia is one of Southeast Asia largest countries and it faces significant cyber threats, and yet issues relating to cyber security in Indonesia are still under explored. The purpose of my research is to investigate how Indonesia securitises cyber security threats, exploring this through the post-Copenhagen School (CS) of securitisation. Post-CS securitisation was selected, not only because this approach emphasises the speech act conducted by the securitising actor, but it also provides room for analysis on the audience’s response in the securitisation process. Securitisation is created by a speech act, a discursive practice that labels an issue as a security threat, thereby legitimising extraordinary measures being taken to address it. Through a social and political lens, cyber security can be viewed as a holistic response to the security challenges posed by information technologies, that have vulnerabilities to threats that are not entirely preventable and are unpredictable in nature. Therefore, cyber security allows for the minimisation of risk posed by threats across information and communication technologies
My research aims to answer the question of how cyber has been securitised in Indonesia between 2019-2024. A key method used to help answer this question is discourse analysis. Discourse analysis focuses on the interpretation of linguistic forms of communication, spoken or written and can include both official and unofficial forms of communication within a social context that contributes to representation and meaning. The speech act theory of securitisation relies heavily on tools of discourse analysis to make claims about how actors use discourse to securitise an issue, or to frame an issue in security terms to allow for stronger policy interventions. It sees the language as performing roles that go beyond describing material reality and sees language as playing an important role in structuring an action. Furthermore, discourse analysis it enables exploration of how speech acts are capable of securitising cyber threats from the perspectives of the securitising actor and the audience. The securitising actor in this sense is the government and the audience is the citizen where the government is undertaking the securitisation.
The application of the research methods used in this research consists of data collection, from law, bills, amendments, speeches and contributions of related officials, regulations, recommendations, press releases, press conferences, interviews, lectures, conference presentations, articles, books and other relevant documents to cyber security issues in Indonesia between 2019 and2024. These documents are drawn from both the government and the audience The research then moves to content and document analysis, by using NVivo software for categorising text and analysing the frequency of different phrases and sentiment through coding. The aim of the analysis is to answer the research questions by exploring the relevant text, conducting categorisation, narrowing through contextual analysis and content structuring/theme analysis according to the theoretical framework. The analytical unit focuses on cyber security, cyber threats, cybercrime, data privacy, data protection, data security, and critical infrastructure. The results will then be analysed through the post-CS securitisation framework to understand how the cyber securitisation discourse evolved between the government and its citizens in terms of securing cyber from 2019 to2024. It is expected that the results of my research will contribute to explaining the relationship between these two actors to inform more effective and contextually relevant cyber security policies in Indonesia for the future.

Kanwal Shahbaz1, Bruno Shivinski2, Kaiden Hein1, Vasileios Stavropoulos1
1 School of Health and Biomedical Sciences, Royal Melbourne Institute of Technology, Australia
2 School of Media and Communication, Royal Melbourne Institute of Technology, Australia
Correspondence: Kanwal.shahbaz@rmit.edu.au
Abstract
Introduction: Digital screens take an increasingly substantial portion of children’s time with unclear detrimental associations with neuronal activity in the developing brain and likewise its connectivity with the behavior among adolescents. To address this global issue, we investigated the association of screen usage time (TV, video games, online video, social media, texting, video chat) including social media and video game addiction with structural and volumetric changes in the brain.
Methods: For this purpose, we assessed the data from the largest longitudinal ABCD study, started at the ages of 9-10 and following participants for 10 years, including a diverse sample of around 12,000 youth enrolled at 21 research sites across the country. We investigated one principal hypothesis that excessive screen usage time is associated with structural and volumetric changes in the brain. The changes in the brain were assessed through structural Magnetic Resonance Imaging sMRI and Diffusion Tensor Imaging (DTI) using the sMRI-T2 intensity White Matter and DTI (Inner shell) Fractional Isotropy white Matter through Destrieux Brain Atlas-72 ROI focused on the white matter of Anterior Cingulate Cortex across 3-time points (T0, T2, T4).
Results:
Using the longitudinal Network Analysis, the results did not support the structural changes in brain however at baseline connectivity is evident between Social Media, Texting, and video chat with the Right hemisphere Cingulate Gyrus and Sulci and Right Marginal branch of the Cingulate Gyrus Sulci compared to other brain regions. Likewise, Social Media and Texting and the Right and Left Posterior Ventral Cingulate Gyrus have shown high centrality with significant global strength of the network. The study is under development at the School of Health and Biomedical Sciences, RMIT, Melbourne, Australia.
Discussion:
The findings highlight the significance of connections between screen usage and volumetric changes in the brain. The brain regions associated with inhibitory control, motivation, and error monitoring are at risk with increased use of digital media. However, the participant's subjective experience and the content of the media usage shall be further considered for an in-depth mapping of developing brain helping to target community-based interventions related to healthy screen habits for flourishing brain.
Keywords: adolescents; digital media use; longitudinal network analysis; MRI, Diffusion Tensor Imaging, White Matter, Anterior Cingulate Cortex

Effectively communicating complex or nuanced subjects as diverse as ‘probabilistic uncertainty’ and ‘cultural capital’, is an ongoing challenge for researchers, policy makers and practitioners within the Queensland Drought and Climate Adaptation Program (DCAP).

In DCAP, historical climate records and seasonal outlooks are increasingly important sources of information for agriculture and natural resource management decision making. To improve the use of this data, primary producers and land managers need to be able to understand and accurately interpret the statistical measures commonly used to present probabilistic climate data, such as percentiles and terciles. Unfortunately, these measures are frequently misinterpreted.

Coinciding with these misinterpretations, related social research had identified several cultural constraints to drought preparedness within Queensland’s extensive grazing and livestock production industry.

Employing a user-centred design (UCD) framework, that makes grazing land managers’ priorities and experiences the centre of focus, animation was combined with character driven storytelling, that communicate scientific and management decision making information. Thus, three short videos in a series were produced; the specific objectives of these animations were to:

• demonstrate how to calculate and interpret rainfall percentiles for a grazing enterprise (“Understanding percentiles in climate data: This season might not be as great as you think it is, Jim”);
• calculate and interpret rainfall and pasture terciles for a grazing enterprise (“Terciles for Climate and Pasture Growth – more than just a median value”); and
• enhance the cultural capital of drought preparedness and resilience (“From a big wet to a dry spell – Jim’s property auction experience”).

The team invested considerable effort in engaging likely users to review and collect feedback on the animation, at various stages in the process (e.g. concept, storyboard, animatic and animation), to ensure it would connect contextually and with its intended viewers. This included using relevant language and phrases, ensuring the landscape, animals and even clothing were appropriate, and adding the character of ‘Rusty’, a farm-style dog, to provide comic relief. It was a careful balance to make sure the animation engaged viewers, but the messages remained technically accurate, and evidence based.

Similarly, several established concepts from behavioural and social science were deliberately but subtly incorporated into the animations, such as using relatable ongoing characters; simple language; a story format; a salient context; a positive frame; credible messengers; social norms; attractive and easy visuals; written and verbal script; humour (affect); and linking the message to known livestock producer values, motivations and aspirations.

Employing a combination of UCD, animation and storytelling with behavioural insights has proven to be an effective and entertaining way to communicate diverse complex and nuanced subjects.

This presentation focuses on the challenges and contributions of an ethnographic approach to the learning experiences of airline cabin crew members as they adapt to their dynamic work environments. Grounded in sociocultural theories of learning, particularly Lave and Wenger's (1991) situated learning concepts, this research aims to understand how cabin crew develop the necessary skills to operate in socially dynamic, safety-critical work contexts. I will explore how ethnographic methods can be applied to workplace learning in complex settings, contributing to ongoing discussions about the evolution of social science research.

Conducting ethnographic research in the highly regulated and fast-paced aviation industry posed significant challenges. First, getting permission to observe training sessions and live flight operations involved navigating organisational protocols and balancing safety concerns with the need for detailed observation. Once in the field, I had to adjust to the dynamic nature of the workplace, where crew members frequently changed flights, interacted with different colleagues and customers, and worked across various routes. Later, consistently tracking individual learning journeys required flexibility and perseverance in my data collection approach. For example, when participant observation was limited, interviews and casual conversations helped me follow their experiences and maintain our connections. During eight months of fieldwork, I could interpret social interactions, tacit knowledge, and context that shaped the learning experiences of new crew members transitioning from training to real-world flight operations.

Additionally, I carefully navigated my dual role as observer and participant. While immersed in the cabin crew's environment, I balanced engaging with participants and respecting their confidentiality, as well as the operational demands, especially during high-stress situations such as assessments and disrupted operations. Despite the challenges, this role adaptation enriched my insights into how cabin crew negotiate both explicit and tacit knowledge under real-world conditions. This immersion also allowed me to capture how learning is intertwined with everyday experiences, showing how ethnography can adapt to complex work environments.

This study contributes to social science research methodology, particularly vocational learning and professional development. The approach provides access to informal, context-specific learning often overlooked by more structured methods such as quantitative assessments, surveys, or retrospective interviews. It uncovers the social and cultural dimensions of learning that occur as cabin crew members participate in real-time challenges, interact with colleagues and customers, and engage in decision-making during flights. Moreover, the research advances our understanding of how professional identities are formed through situated learning in complex workplaces. By focusing on the negotiation between formal training and informal learning, the study highlights the value of ethnography for examining how individuals and teams develop expertise in high-pressure, safety-critical environments. These insights enrich the literature on workplace learning and contribute to broader methodological discussions about ethnography in modern work contexts.

Adolescents most in need of wellbeing support are the least likely to seek support independently. Self-reports are most reliable way to measure wellbeing, however, such reports are not always accessible or cost effective to obtain. This highlights the challenge of identifying those most in need. Through novel WhatsApp instant message interviews, we asked adolescents, the knowledge experts in this area.
1. Who can make a judgement of your wellbeing?
2. How do you communicate your low wellbeing?
3. With whom do you communicate you low wellbeing?
Conducting qualitative interviews using digital messages through WhatsApp, we engaged with young people in their native space. This format limited or reversed the power differential between the interviewer and interviewee, increased the level of anonymity, and gave the adolescent participants full freedom and control within the interview. The data collected was rich, and verbose, and the young people commented on how they enjoyed these interactions.
The research design successfully overcame the key challenge of engaging with adolescents from a general, non-clinical population. By meeting the young people in their own environment; online and conducting interviews in a setting and at a time that that was convenient for them highlighted that the young person was the key driver of these interactions. The interviewer was careful not to place any logistic or systematic barriers between her and the participants. This was done by timing of interviews that were most comfortable for the young person, the endless possibility of locations for both the interviewer and interviewee, and not putting any rules or guidelines around language, speed of response, or discussing interruptions. The outcome, was young people connecting fully, offering long and clear responses, and having free flowing back and forward conversation.
Through grounded theory thematic analysis, the data collected has provided valuable insights into the nuanced ways young people navigate the judgements of others and have implications for supporting young people's mental health and wellbeing. The key findings from this study were firstly that mothers are given the opportunity to hear and see how the young person is feeling. Secondly, fathers may see how a young person feels; however, it is less likely the young person will speak to dad about low feelings. Thirdly, Teacher may benefit to see how a young person feels, due to school environmental factors, where young people are interacting with friends. Lastly, friends are a safe space for verbal and non-verbal disclosure of low feelings. With future research we will aim to create guidelines for how parents, and teachers may can engage with young people where the this is an opportunity to see or hear about their low feelings.

This paper explores the emerging role of artificial intelligence in humanistic marketing approaches. We examine how AI technologies can enhance customer-centric strategies while maintaining a focus on human values and experiences. The research investigates AI applications in personalization, emotional intelligence, and ethical decision-making within marketing contexts. Through four case studies of organisations that have used AI in innovative ways to boost consumer centrism and interviews with key people behind those innovative applications , we explored the potential of AI to foster more meaningful brand-consumer relationships and drive social impact. Our findings suggest that when properly implemented, AI can in fact augment rather than replace human elements in marketing from the content creation to consumer interactions. This will lead to more empathetic and value-driven marketing where human element in pronounced rather than stifled by AI. That said, we also identify key challenges, including data privacy concerns and the risk of algorithmic bias where AI decision making could be erroneous in and misleading marketing campaigns. The paper concludes with recommendations for marketers on leveraging AI to create more human-centric, socially responsible marketing strategies that resonate with increasingly conscious consumers.

In our recent research, we were unable to collaborate face-to-face to conduct a joint data analysis between researchers from the UK and Australia due to COVID-19, which prevented researchers from the UK from travelling to Australia. We ended up doing this collaborative data analysis using a boundary object (a visual template) to conduct joint data analysis over a limited time (due to geographical time constraints) and develop our theoretical contribution using a template analysis tool that we have not used before. We will explain the use of template analysis as a boundary object to conduct collaborative research.

Background: The demand for person-centered care has led to innovations in capturing patient feedback to assess hospital care quality. Traditionally, most insights into hospital care experiences have come from quantitative surveys completed post-discharge. However, there is growing recognition that real-time qualitative feedback collected during hospital stays provides richer, more actionable information for improving care. Despite this, existing approaches rarely offer guidance on how to deliver this feedback effectively to nurses, nor do they engage key stakeholders—such as nurses, patients, and families—in designing the feedback collection and dissemination process.

Objective: This study aimed to explore how stakeholders could collaboratively create a protocol for collecting and sharing real-time patient and family feedback with nurses to ensure that these perspectives are effectively integrated into care practices.

Method: A pragmatic action research approach was used, involving an Advisory Group composed of patients, family members, and nurses (n=16). This group co-designed a protocol for capturing qualitative feedback at the unit level during hospital stays. The research unfolded in two phases: the first involved identifying the problem and gathering initial insights, while the second phase (Action Cycles 1 to 9) focused on developing and evaluating the new feedback protocol, known as RHEPORT (Real-time Hospital Experience Posters).

Results: The study generated two key data sets: feedback from patients and family members on their hospital experiences, and evaluation data on the effectiveness of the RHEPORT protocol. Over three years and nine Action Cycles, 241 participants provided feedback, highlighting important themes such as physical comfort, respect for patient values and needs, and the quality of information, communication, and education provided. Additionally, 407 evaluation responses were collected from patients, visitors, and nurses. The findings demonstrated that the pragmatic action research approach successfully co-created the RHEPORT protocol, which features five core components essential for meaningful feedback collection and distribution. The study underscores the importance of involving stakeholders in developing feedback protocols to ensure their voices are truly heard and acknowledged.

The relationship between law and language is intricate and fundamental. Law depends on language for its existence, with legal norms constructed using linguistic elements from various domains (Majela Ferrari Yaunner, 2020). This connection is complex due to the diverse nature of legal content, leading to a unique legal language that incorporates terms from other fields. Challenges in legal interpretation arise from linguistic obstacles such as vagueness and ambiguity in norms, which can be considered characteristics of legal language (Majela Ferrari Yaunner, 2020).
The linguistic turn in law represents a significant shift in legal philosophy, focusing on the relationship between language and law (E. Carbonell, 2020). This perspective has led to methodological changes that can transform legal science. H.L.A. Hart's "The Concept of Law" (1961) is considered a turning point in legal philosophy, introducing analytical approaches compatible with diverse content (Cristóbal Orrego Sánchez, 1996). In legal theory, the logical-semantic constructivism approach applies linguistic turn principles to develop a General Theory of Law. This theory encompasses three main components: Theory of Legal Norms, Theory of Incidence, and Theory of Legal Order, all grounded in the philosophical foundations of the linguistic turn (Aurora Carvalho, 2009). These developments highlight the profound influence of linguistic analysis on legal thought and methodology.
Thus, it is appropriate to analyze the relationship between law and language, and in particular, the language in which normative statements are expressed, and the norms that constitute their meanings, but the researcher not only has to identify the material, but also make decisions about its meaning. The problem is that normative statements are not clear, so methods that allow interpretation are necessary.
The interpretative task is linked to that of justifying the choice or attribution of meaning. Similarly, in legal interpretation there are various problems derived from the fact that norms are the meaning of a type of statements, and the interpretative activity aims to determine the meaning of the norms.
Thus, the interpretative process and its different phases are an essential part of the justification of the legal decision, that is, of the law.
In this context, this research delves into the different methods and perspectives of analysis of legal language.