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Reflections on 50 years of sentencing reform:the good, the bad and the future
No full textSentencing reform in Australia over the past 50 years has reflected ideological conflicts and deeper structural transformations in the economy and in society. It is a tale of two paradigms, one which is inclusive, participatory and rehabilitative, the other punitive and populist. This article identifies many of the positive changes over that period such as the recognition of the role of victims in the criminal justice system, the introduction of diversion programs, restorative justice and problem-oriented courts, the growth of trauma-informed sentencing and the creation of sentencing advisory councils. It also notes the rise of the prison population. The influence of penal populism has produced mandatory sentencing laws, restrictions on parole and post-release dispositions for those deemed to be too dangerous to be released into the community. Finally, it notes the possible role of artificial intelligence in sentencing.</p
Descriptor: fear and fear regulation of Chinese and Vietnamese investors in the extremely volatile markets dataset (FearCVI)
No full textEmotions are fundamental elements driving humans’ decision-making and information processing. Fear is one of the most common emotions influencing investors’ behaviors in the stock market. Although many studies have been conducted to explore the impacts of fear on investors’ investment performance and trading behaviors, little is known about factors contributing to and alleviating investors’ fear during the market crash (or extremely volatile periods) and their fear regulation after the crisis. Thus, the current data descriptor provides details of a dataset of 1526 Chinese and Vietnamese investors, a potential resource for researchers to fill in the gap. The dataset was designed and structured based on the information-processing perspective of the Mindsponge Theory and existing evidence in life sciences. The Bayesian Mindsponge Framework (BMF) analytics validated the data. Insights generated from the dataset are expected to help researchers expand the existing literature on behavioral finance and the psychology of fear, improve the investment effectiveness among investors, and inform policymakers on strategies to mitigate the negative impacts of market crashes on the stock market
Ethical challenges in conducting research in low and middle income setting during public health emergencies:a qualitative evidence of a COVID-19 pandemic: the experience of Iran
No full textBackground: Every minute during an epidemic is important and research in such conditions is for the benefit of the society. Considering that identifying experiences is a way to prevent repeated mistakes and prepare people to face crisis situations, this study aimed to explain participants’ experiences of ethical challenges encountered in conducting research related to Covid-19 in Iran. Method: This qualitative study was carried out using conventional content analysis for 2 years from March 2020 to March 2022 in Tehran, Iran. A number of 30 people were selected in a purposeful method and information was obtained through semi-structured interviews. The participants in the study were people with positions including members of institutional and national research ethics committees, researchers, clinicians, university hospitals managers during the COVID-19 pandemic. The method of data analysis in this study was conventional content analysis using the Graneheim and Lundman method. Results: Participants’ experiences on ethical challenges were explained through three themes: “substantive ethical values principles”, “the Research Environment”, “Research Governance and Management”. Conclusion: This study examines ethical challenges in COVID-19 research across three domains: values, environment, and research governance. The results suggest the need to develop crisis-specific ethical frameworks, strengthen research ethics infrastructure and training, and establish more transparent standards and oversight systems. These findings could be useful in refining ethical policies and managing future crises.</p
Making Visible the Australian Frontier Wars and Comparative Memorialization
No full textThis chapter draws on research conducted as part of the Representation, Remembrance and the Memorial (RRM) project, which considered the memorialization of the Frontier Wars in Australia in an international context to investigate the possibility of an Australian memorial that shifts public consciousness in ways comparable to the effect of monuments to the Holocaust, genocide, war, and state violence in other countries. The authors used a variety of research methods such as interviews, site visits, and forum activities with community leaders, artists, historians, and memory scholars and architects in an approach that prioritised lived experience. The case study for this chapter is the proposed National Resting Place for unprovenanced Aboriginal and Torres Strait Islander ancestral remains in Canberra
Does remote work adoption boost firm innovation? A cross-cultural study
No full textThe unprecedented large-scale remote work practices during the COVID-19 pandemic have demonstrated the effectiveness of this new way of working. However, previous findings regarding the influence of remote work adoption on firm innovation have been inconsistent. Building upon the culture fit perspective, the current study aims to examine the vital role of national culture in shaping the relationship between remote work adoption and firm innovation. Specifically, we propose that the adoption of remote work will foster firm innovation, particularly when the cultural characteristics are congruent with the nature of remote work. Based on multi-wave data collected from 8,053 firms across 21 countries, research findings from our multilevel analysis suggest that the positive effect of remote work adoption on firm innovation was stronger in nations with low power distance, high indulgence, and short-term orientation. The current study sheds light on the cultural factors in remote work practices and also has practical implications for organizations transitioning to remote or hybrid work in the post-COVID-19 era.</p
Trends in retention and attrition in nine regulated health professions in Australia
No full textObjective: To identify factors associated with the retention and attrition of regulated health practitioners in Australia across nine health professions. Methods: An online survey of practitioners and an analysis of 10 years of Australian Health Practitioner Regulation Agency (Ahpra) registration data were carried out. Results: Among surveyed health practitioners, 20,449 (79.4%) intended to stay, 1368 (5.3%) intended to leave, and 1759 (6.8%) were unsure. Most intending to leave planned to do so immediately or within 1-year (72.8%). Top reasons for leaving included mental burnout (32.9%), retirement (30.5%), feeling undervalued/unrecognised (28.5%), lack of professional satisfaction (27.9%), and work no longer being fulfilling (25.1%). Men, older practitioners, those working fewer than 20 h per week, and non-self-employed practitioners were more likely to consider not renewing or to be unsure. Analysis of Ahpra registration data from 2014 to 2023 showed that the number of registered practitioners per 100,000 population increased by 29.6%, but the replacement rate showed notable fluctuations over the observed period. Females consistently exhibited higher replacement rates compared to males, with exits from the workforce highest in those aged under 35 pre-2020 and highest in those aged 35-60 post-2020. Conclusions: Although the overall number of health practitioners increased from 2014 to 2023, replacement rates have been fluctuating, highlighting concerns about workforce stability, particularly among males, older practitioners, those working fewer or greater than full-time hours, and non-self-employed practitioners. Addressing intrinsic and workplace factors such as mental burnout, lack of recognition, and job satisfaction may improve retention.</p
Data-driven recommendations for enhancing real-time natural hazard warnings
No full textThe effectiveness of natural hazard warnings relies on transforming the available data into actionable knowledge for the public. Real-time warning communication and emergency response therefore need to be evaluated from a data-driven perspective. However, gaps exist between established data science best practices and their application in supporting natural hazard warnings. This perspective reviews existing data-driven approaches, highlighting limitations in hazard and impact warnings. Four main themes emerge for enhancing warning communication and supporting decision-making: (1) identifying data-barriers to effective warnings, (2) applying best-practice principles in visualizing warnings, (3) utilizing novel data for more localized forecasts and warnings, and (4) improving data-driven decision-making using uncertainty. These themes are illustrated using the extensive flooding in Australia in 2022 as a case study. This perspective reveals opportunities for improving the efficacy of natural hazard warnings using data science, and the collaborative potential between the data science and natural hazards communities
Semiparametric single-index estimation for average treatment effects
No full textWe propose a semiparametric method to estimate the average treatment effect under the assumption of unconfoundedness given observational data. Our estimation method alleviates misspecification issues of the propensity score function by estimating the single-index link function involved through Hermite polynomials. Our approach is computationally tractable and allows for moderately large dimension covariates. We provide the large sample properties of the estimator and show its validity. Also, the average treatment effect estimator achieves the parametric rate and asymptotic normality. Our extensive Monte Carlo study shows that the proposed estimator is valid in finite samples. Applying our method to maternal smoking and infant health, we find that conventional estimates of smoking’s impact on birth weight may be biased due to propensity score misspecification, and our analysis of job training programs reveals earnings effects that are more precisely estimated than in prior work. These applications demonstrate how addressing model misspecification can substantively affect our understanding of key policy-relevant treatment effects.</p
Finnish teacher students’ career choice motivations:a mixed methods study
No full textThis mixed-methods study analysed motivations to teach among Finnish first-year teacher students (N = 946) from early childhood, primary and special education programs, to discern potential additional motivations in a context where teaching is highly regarded and any differences between programs. Responses to an open-ended question revealed complementary motivational nuances to enrich the FIT-Choice scale in this context, such as ‘perceived social status’, highlighting the value of a mixed-methods approach. Highest-rated motivations on the FIT-Choice scale were social and intrinsic values, ability, and positive prior teaching and learning experiences; these were also most frequently mentioned of the FIT-Choice factors in open-ended responses, although correspondence at the individual level was modest. Minor differences occurred in career motivations between students from different programs.</p
The Sky is the Limit:Understanding How Generative AI can Enhance Screen Reader Users' Experience with Productivity Applications
No full textProductivity applications including word processors, spreadsheets, and presentation tools are crucial in work, education, and personal settings. Blind users typically access these tools via screen readers (SRs) and face significant accessibility and usability challenges. Recent advancements in Generative AI (GenAI) may address these challenges by enabling natural language interactions and contextual task understanding. However, there is limited understanding of SR users' needs and attitudes toward GenAI assistance in these applications. We surveyed 99 SR users to gain a holistic understanding of the challenges they face when using productivity applications, the impact of these challenges on their productivity and independence, and their initial perceptions of AI assistance. Driven by their enthusiasm, we conducted interviews with 16 SR users to explore their attitudes toward GenAI and its potential usefulness in productivity applications. Our findings highlight its need to support existing SR workflows and the importance of enabling customization and task verification.</p