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The Perfect Storm of Cyberscam Risk:Examining Personal, Injury, and Psychosocial Risk Factors for People With and Without Acquired Brain Injury
No full textCyberscams are a pervasive global issue with losses exceeding $1 trillion worldwide and resulting in significant psychosocial impacts, particularly shame. People with disabilities, such as acquired brain injury (ABI), may be additionally vulnerable due to cognitive impairments and social isolation. Increased scam vulnerability and risk factors for people with ABI have not been investigated. This study aimed to (a) determine whether people with ABI have greater risk of being scammed than people without ABI, and (b) explore demographic and psychosocial factors associated with cyberscam risk for people with and without ABI. Using a cross-sectional design, participants with (n = 149) and without (n = 153) ABI provided scam experience details and completed a validated measure of self-rated cybersafety and practical scam identification (The CyberAbility Scale) and measures of psychosocial risks of loneliness, impulsivity, mood, trust, and community integration. Correlation analyses showed that participants with ABI performed worse on a scam identification task than those without ABI. As expected, higher self-rated scam safety was associated with lower loneliness, impulsivity, and fewer mood symptoms, and higher trust and community integration. In multiple regression analyses, higher loneliness was most significantly associated with higher self-rated cyberscam risk, and older age and presence of ABI were associated with poorer scam identification. This study illustrates the multifaceted nature of cyberscam risk, involving distinct social and knowledge-based risks. Findings underscore the need for scam prevention and recovery initiatives targeting at-risk groups and considering the needs of people with ABI in staying safe online.</p
Bootstrapping non-stationary and irregular time series using singular spectral analysis
No full textThis article investigates the consequences of using Singular Spectral Analysis (SSA) to construct a time series bootstrap. The bootstrap replications are obtained via a SSA decomposition obtained using rescaled trajectories (RT-SSA), a procedure that is particularly useful in the analysis of time series that exhibit nonlinear, non-stationary and intermittent or transient behaviour. The theoretical validity of the RT-SSA bootstrap when used to approximate the sampling properties of a general class of statistics is established under regularity conditions that encompass a very broad range of data generating processes. A smeared and a boosted version of the RT-SSA bootstrap are also presented. Practical implementation of the bootstrap is considered and the results are illustrated using stationary, non-stationary and irregular time series examples.</p
Short selling and product market competition
No full textWe empirically investigate how short selling affects firms’ product market performance via a managerial monitoring channel. Using both historical data and exogenous shocks to short selling, we find robust evidence that short interest negatively impacts market shares, especially in large firms. Our Reg SHO results are stronger in concentrated industries and industries where firms compete in strategic substitutes. Further tests show that these effects are driven by low ex-ante stock price informativeness. The evidence suggests that the interaction between market power and price opacity generates incentives for overproduction, which short selling attenuates. Our results support policies that facilitate price discovery in the presence of market power.</p
Gendered recordkeeping practices in marginalised communities in Bangladesh
No full textIntroduction. Little is known about gendered recordkeeping practices in marginalised communities in developing countries and about how these practices have been affected by improvement in literacy in those communities. Method. This paper reports the results of semi-structured interviews with 20 women and 17 men in two remote areas of Bangladesh about their recordkeeping practices. Results. This paper shows that the female interviewees tend to preserve information more frequently than the male interviewees and that women are assuming more responsibilities in relation to keeping the important records of their family. The greater role assumed by women can be related to a rapid improvement in women’s literacy in the past 30 years. However, comments made by interviewees show that this greater responsibility attributed to the female members of the households is not a reflection of women’s empowerment. At the contrary, it is often an additional burden imposed on them since the women write down what their husbands expect them to write and keep the records that their husbands ask them to keep. Conclusion. This study highlights the importance of doing more research on gendered recordkeeping practices and on their connections with women’s empowerment in marginalised communities.</p
Modifying AI, Enhancing Essays:How Active Engagement with Generative AI Boosts Writing Quality
No full textStudents 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
Self-regulated Learning Processes in Secondary Education:A Network Analysis of Trace-based Measures
No full textWhile the capacity to self-regulate has been found to be crucial for secondary school students, prior studies often rely on self-report surveys and think-aloud protocols that present notable limitations in capturing self-regulated learning (SRL) processes. This study advances the understanding of SRL in secondary education by using trace data to examine SRL processes during multi-source writing tasks, with higher education participants included for comparison. We collected fine-grained trace data from 66 secondary school students and 59 university students working on the same writing tasks within a shared SRL-oriented learning environment. The data were labelled using Bannert's validated SRL coding scheme to reflect specific SRL processes, and we examined the relationship between these processes, essay performance, and educational levels. Using epistemic network analysis (ENA) to model and visualise the interconnected SRL processes in Bannert's coding scheme, we found that: (a) secondary school students predominantly engaged in three SRL processes - Orientation, Re-reading, and Elaboration/Organisation; (b) high-performing secondary students engaged more in Re-reading, while low-performing students showed more Orientation process; and (c) higher education students exhibited more diverse SRL processes such as Monitoring and Evaluation than their secondary education counterparts, who heavily relied on following task instructions and rubrics to guide their writing. These findings highlight the necessity of designing scaffolding tools and developing teacher training programs to enhance awareness and development of SRL skills for secondary school learners.</p
Turning Real-Time Analytics into Adaptive Scaffolds for Self-Regulated Learning Using Generative Artificial Intelligence
No full textIn computer-based learning environments (CBLEs), adopting effective self-regulated learning (SRL) strategies requires sophisticated coordination of multiple SRL processes. While various studies have proposed adaptive SRL scaffolds (i.e. real-time advice on adopting effective SRL processes) and embedded them in CBLEs to facilitate learners' effective use of SRL strategies, two key research gaps remain. First, there is a lack of research on SRL scaffolds that are based on continuous assessment of both learners' SRL processes and learning conditions (e.g., awareness of learning resources) to provide adaptive support. Second, current analytics-based scaffolding mechanisms lack the scalability needed to effectively address multiple learning conditions. Integration of analytics of SRL with generative artificial intelligence (GenAI) can provide scalable scaffolding for real-time SRL processes and evolving conditions. Yet, empirical studies implementing and evaluating effects of this integration remain scarce. To address these limitations, we conducted a randomized control trial, assigning participants to three groups (control, process only, and process with condition groups) to investigate the effects of using GenAI to turn insights from real-time analytics about students' SRL processes and conditions into adaptive scaffolds. The results demonstrate that integrating real-time analytics with GenAI in adaptive SRL scaffolds - addressing both SRL processes and dynamic conditions - promotes more metacognitive learning patterns compared to the control and process-only groups. In addition, the learners showed varying levels of compliance with analytics-based GenAI scaffolds, and this was also reflected in how the learners coordinated their SRL processes, particularly in the performance phase of SRL. This study contributes to the literature by designing, implementing, and evaluating the impact of adaptive scaffolds on learners' SRL processes using real-time analytics with GenAI.</p
Measuring the potential for hateful behaviours:development and validation of the Hate Behaviours Scale (HBS)
No full textThis article introduces and reports the psychometric properties of the Hate Behaviours Scale (HBS), which assesses individual intentions to engage in a range of violent and non-violent hateful behaviours against various target groups. Three independent U.S. adult samples (total n = 3524) were used to gather data on the scale and associated covariates and constructs. The twelve-item HBS is comprised of three subscales; Discrimination, Defensive Violence and Belligerent Violence. The higher scores on the HBS were significantly associated with past behaviours such as attending a protest against the target group (β = 0.57, p < .001), having physically hit a member of the target group (β = 0.57, p < .001), and sharing an offensive joke online about the target group (β = 0.51, p < .001). This new measure provides a modifiable, easy to use, reliable and valid multi-component scale for assessing hate against different target groups. It should allow researchers to reliably assess individual levels of hate, and establish relationships with other demographic, social, situational, and psychological characteristics. The HBS provides a short and cost-effective tool to inform and evaluate counter violent extremism interventions aimed at reducing the potential for hateful behaviours.</p
A blueprint for large language model-augmented telehealth for HIV mitigation in Indonesia:A scoping review of a novel therapeutic modality
No full textBackground: The HIV epidemic in Indonesia is one of the fastest growing in Southeast Asia and is characterised by a number of geographic and sociocultural challenges. Can large language models (LLMs) be integrated with telehealth (TH) to address cost and quality of care? Methods: A literature review was performed using the PRISMA-ScR (2018) guidelines between Jan 2017 and June 2024 using the PubMed, ArXiv and semantic scholar databases. Results: Of the 694 records identified, 12 studies met the inclusion criteria. Although the role of eHealth interventions as well as telehealth in HIV management appears well established, there is a significant literature gap on the integration of telehealth and LLM technology. To address this, we provide a blueprint for the safe and ethical integration of LLM-TH into triage, history taking, patient education highlighting opportunities for reduced consultation time and improved quality of care. Conclusions: Variable access to mobile technology and the need for empirical validation stand out as limitations for LLM-TH. However, we argue that the current evidence base suggests the benefits far outweigh the challenges in applying LLM-TH for HIV care in Indonesia. We also argue this novel therapeutic modality is broadly applicable to the subacute general practice setting.</p
Rural-urban migration and household transportation expenditures:a causal exploration method using Indonesian panel data
No full textDespite extensive studies on the interaction between transport and land use, the literature has largely overlooked whether exposure to significantly denser environments after relocating from low-density settings influence mobility outcomes. This study addresses this gap by proposing a causal framework to estimate the impacts of rural–urban migration on household transportation expenditures. Using the longitudinal Indonesian Family Life Survey (IFLS), Difference-in-Differences (DiD) models are estimated to a balanced panel dataset, comprising households that relocated to urban areas (exposed group) and similar households that migrated to rural areas (comparison group) identified through Propensity Score Matching (PSM). The results suggest statistically insignificant reduction in the share of transportation expenditures attributed to the cumulative exposure to dense and mixed-use urban environments.</p