‘Unsolvable within existing regimes’: Using a Systems Thinking Approach to Co-design for Data Governance in Cities

Despite people’s significant role in generating data in cities, their involvement in data governance (DG) remains limited, failing to address the inherent complexity of DG and undermining their ’right to the city’. We propose a collaborative systems thinking approach as a scoping tool for co-design, enabling researchers and designers to involve people in co-creating an understanding of the systemic structures underpinning DG in cities and developing prototypes and solutions informed by these structures. Using causal loop diagrams, we facilitated the development of a conceptual model of DG. Participants, representing diverse perspectives, created individual causal loop diagrams that were merged into a collaborative causal loop diagram (C-CLD). This C-CLD was employed in an interactive workshop to identify intervention points and develop targeted solutions. Our findings demonstrate how C-CLDs can accommodate multiplicity, foster agonism, and enable participants to challenge political dimensions and existing systemic structures. Moreover, the engagement process revealed the complexity of DG in the city, as perceived by the collective of participants, resulting in three key submodules that highlight tensions between citizen sensitisation to data collection, the private sector’s role in fulfilling citizens’ needs, and the struggles faced by local governments. This work draws on and extends HCI research that engages with systems thinking ontologies, contributing to an HCI that includes the political, moves beyond solutionism, and advances social justice-oriented approaches

Explainable machine learning prediction of internet addiction among Chinese primary and middle school children and adolescents:a longitudinal study based on positive youth development data (2019–2022)

Background: Internet Addiction (IA) has emerged as a critical concern, especially among school age children and adolescents, potentially stalling their physical and mental development. Our study aimed to examine the risk factors associated with IA among Chinese children and adolescents and leverage explainable machine learning (ML) algorithms to predict IA status at the time of assessment, based on Young’s Internet Addiction Test. Methods: The longitudinal data consisting of 8,824 schoolchildren from the Chengdu Positive Child Development (CPCD) survey were analyzed, where 33.3% of participants were identified with IA (Age: 10.97 ± 2.31, Male: 51.73%). IA was defined using Young’s Internet Addiction Test (IAT ≥ 40). Demographic variables such as age, gender, and grade level, along with key variables including scores of Cognitive Behavioral Competencies (CBC), Prosocial Attributes (PA), Positive Identity (PI), General Positive Youth Development Qualities (GPYDQ), Life Satisfaction (LS), Delinquent Behavior (DB), Non-Suicidal Self-Injury (NSSI), Depression (DP), Anxiety (AX), Family Function Disorders (FF), Egocentrism (EG), Empathy (EP), Academic Intrinsic Value (IV), and Academic Utility Value (UV) were examined. Chi-square and Mann–Whitney U tests were employed to validate the significance of the mentioned predictors of IA. We applied six ML models: Extra Random Forest, XGBoost, Logistic Regression, Bernoulli Naïve Bayes, Multi-Layer Perceptron (MLP), and Transformer Encoder. Performance was evaluated via 10-fold cross-validation and held-out test sets across survey waves. Feature selection and SHapley Additive exPlanations (SHAP) analysis were utilised for model improvement and interpretability, respectively. Results: ExtraRFC achieved the best performance (Test AUC = 0.854, Accuracy = 0.798, F1 = 0.659), outperforming all other models across most metrics and external validations. Key predictors included grade level, delinquent behavior, anxiety, family function, and depression scores. SHAP analysis revealed consistent and interpretable feature contributions across individuals. Conclusion: Depression, anxiety, and family dynamics are significant factors influencing IA in children. The Extra Random Forest model proves most effective in predicting IA, emphasising the importance of addressing these factors to promote healthy digital habits in children. This study presents an effective SHAP-based explainable ML framework for IA prediction in children and adolescents.</p

Visceral afferent training in action:the origins of agency in early cognitive development

The foetal period constitutes a critical stage in the construction and organisation of the mammalian nervous system. In recent work, we have proposed that foetal brain development is structured by bottom-up (interoceptive) inputs from spontaneous physiological rhythms such as the heartbeat (Corcoran et al., 2023). Here, we expand this 'visceral afferent training' hypothesis to incorporate the development of top-down (allostatic) control over bodily states. We conceptualise the emergence of cardiac regulation as an early instance of sensorimotor contingency learning that scaffolds the development of agentic control. We further propose that the brain's capacity to actively modify and regulate the afferent feedback it receives through interoceptive channels – and to parse these signals into their self-generated (reafferent) and externally-generated (exafferent) components – is crucial for grounding the distinction between self and other. Finally, we explore how individual differences in the ways these training regimes are implemented (or disrupted) might impact developmental trajectories in gestation and infancy, potentiating neurobehavioural diversity and disease risk in later life.</p

Modifying AI, Enhancing Essays:How Active Engagement with Generative AI Boosts Writing Quality

Students are increasingly relying on Generative AI (GAI) to support their writing - a key pedagogical practice in education. In GAI-assisted writing, students can delegate core cognitive tasks (e.g., generating ideas and turning them into sentences) to GAI while still producing high-quality essays. This creates new challenges for teachers in assessing and supporting student learning, as they often lack insight into whether students are engaging in meaningful cognitive processes during writing or how much of the essay's quality can be attributed to those processes. This study aimed to help teachers better assess and support student learning in GAI-assisted writing by examining how different writing behaviors, especially those indicative of meaningful learning versus those that are not, impact essay quality. Using a dataset of 1,445 GAI-assisted writing sessions, we applied the cutting-edge method, X-Learner, to quantify the causal impact of three GAI-assisted writing behavioral patterns (i.e., seeking suggestions but not accepting them, seeking suggestions and accepting them as they are, and seeking suggestions and accepting them with modification) on four measures of essay quality (i.e., lexical sophistication, syntactic complexity, text cohesion, and linguistic bias). Our analysis showed that writers who frequently modified GAI-generated text - suggesting active engagement in higher-order cognitive processes - consistently improved the quality of their essays in terms of lexical sophistication, syntactic complexity, and text cohesion. In contrast, those who often accepted GAI-generated text without changes, primarily engaging in lower-order processes, saw a decrease in essay quality. Additionally, while human writers tend to introduce linguistic bias when writing independently, incorporating GAI-generated text - even without modification - can help mitigate this bias.</p

International investment law and public health:the need for forward-looking reforms

Although unsuccessful, Phillip Morris' claims against tobacco control measures implemented by Uruguay and Australia drew attention to the potential for public health measures to be challenged under international investment agreements (IIAs). This article examines how states have responded to these and other cases, with recently concluded IIAs now often containing clarifications on the scope of indirect expropriation, exceptions for public health measures, and/or carve-outs from investor-state dispute settlement for tobacco control measures or other public health measures. While these changes in treaty drafting provide some protection for policy space related to public health, this article argues that they are largely reactionary to cases that have been brought in the past, and do not address potential new battlegrounds between investors and states relating to public health, for example, emerging health risks for which we do not yet have any international consensus on the appropriate regulatory response (e.g. e-cigarettes), and the measures that many states are now taking to favour domestic producers to ensure manufacturing capability for key public health supplies and pharmaceuticals in the wake of the COVID-19 pandemic and recent geo-political tensions. This leaves a clear direction for future research into how IIAs could be drafted with flexible mechanisms that safeguard policy space for public health crises in future, which may not have been foreseen at the time of treaty drafting. The article then goes on to consider the scope for future research on how IIAs can, in fact, forward public health, through targeted investment facilitation provisions.</p

Social networking sites use and life satisfaction:a moderated mediation model of e-health literacy, fatigue, uncertainty, and stress

Excessive social media use during health crises can lead to information overload and psychological distress, yet the mechanisms underlying this relationship remain unclear. This study investigated how social networking sites (SNS) affected life satisfaction during the COVID-19 pandemic in Iran and whether this relationship was explained by SNS fatigue, uncertainty about disease, and stress. The research also examined whether e-health literacy was a protective factor in this process. Results confirmed that SNS use negatively affected life satisfaction through a sequential pathway of increased fatigue, uncertainty, and stress. E-health literacy moderated the initial link between SNS use and fatigue, with higher literacy weakening this relationship. The results demonstrate the complex relationship between SNS use and wellbeing during health crises and highlight the potential protective role of e-health literacy.</p

Die hard:exploring the characteristics of resource users who persist in the tragedy of the commons

This field study investigates the characteristics and preferences of artisanal fishers who continue their profession in a lake afflicted by overfishing. We relate their economic preferences, fishing data, social networks, and socio-demographic information to their decision to either persist or discontinue fishing 4 and 15 years later. Our findings reveal that an increasing portion of fishers have chosen to cease fishing over time. We observe that the fisher's risk preference is an important factor for persistence: More risk-averse fishers are more likely to endure in their fishing endeavors. We also find evidence that better socially integrated, older, and less educated individuals are more persistent. In contrast, we do not observe any notable relationships between persistence and the individual extent of overfishing or social preferences. These insights offer valuable novel knowledge regarding the evolving dynamics of resource user groups. By understanding these factors, policymakers and managers can optimize their approach to designing effective management practices and policies.</p

School alumni associations in modern Australia as transcultural conduits of migrant identity:the Sri Lankan experience

The literature illustrates that there has been relatively little specific research undertaken on the ways in which pre-migration school connections might have influenced the individual or group migration experience to Australia. Such influence is evident with migrants from Sri Lanka (Ceylon), a gap that the authors have identified in previous work. This paper attempts to bridge this research space by reporting on a pilot study into how Sri Lankan school alumni associations in Australia have influenced the migration experiences of individuals now living in Australia. Data for the study was generated via surveys and interviews with members of alumni organisations. Findings suggest that Sri Lankan school alumni associations can be important drivers and facilitators of both the act of migration and the process of migrant adjustment. They work as conduits enabling their members to remain connected to their ancestral roots in Sri Lanka whilst embedding themselves in modern Australian society.</p

Canadian educators' post-pandemic recovery and students’ unmet needs:who is left behind?

We investigated post-pandemic recovery in education sector employees by role in 2024. The frameworks of the job demands-resources model and ecological systems theory were employed. Canadian educators (N = 243) completed surveys exploring their mental health (well-being, resilience, recovery), intention to leave their jobs, and their perceptions of students' current and post-pandemic needs. Quantitative findings revealed educators who intended to leave their jobs had poorer levels of mental health. Also, they were not meeting their students’ needs adequately. The qualitative data showed that students with complex needs were disproportionally under-served. Theoretical, practical, and policy implications on equitable education are discussed.</p