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Environmental credit regulatory policies and bank loans of heavily polluting firms
Get PDFWe use the environmental credit rating policy as a quasi-natural experiment to analyze how these policies affect bank loans to heavily polluting firms in China. Utilizing a panel dataset of A-share listed firms and difference-in-differences models, we find that policies (a) reduce the scale and proportion of long-term loans, (b) increase loan costs and financial distress risks, and (c) enhance social responsibility for heavily polluting firms. State-owned firms face stronger regulation, while those in regions with advanced digital financial markets and lower carbon emissions encounter smaller credit constraints.</p
Partitive division:the numbers matter to young children
No full textThis paper contains an analysis of some early thinking of 94 young children aged 5 years 7 months to 6 years 5 months. These children were interviewed as part of a larger study of the multiplicative thinking of children who were midway through their first year of school in Australia. They had not been formally taught multiplication or division at school. Three task-based interview questions were posed using partitive contexts. The children attempted to solve 12 ÷ 3, 7 ÷ 2 and 22 ÷ 4. An analysis of the responses of these children reveals many children can solve division problems even when the numbers get larger and where the divisor is not a factor of the dividend
Silence as a quiet strategy:understanding the consequences of workplace ostracism through the lens of sociometer theory
Get PDFExisting research has predominantly framed defensive silence as an avoidance response to interpersonal mistreatments. Moving beyond this view, this study theorizes defensive silence as a proactive strategy for managing interpersonal relationships through the lens of sociometer theory. We posit that workplace ostracism will reduce employees’ organization-based self-esteem (OBSE), which in turn increases their subsequent defensive silence to avert further damage to relationships. In addition, we also expect a moderating role of the sense of power in mitigating the negative impact of workplace ostracism on OBSE. Based on the multi-wave, multi-source data of 345 employees and their 82 immediate supervisors, we tested all the hypotheses. Results from multilevel modeling indicated that OBSE mediated the indirect effect of workplace ostracism on defensive silence, and also supported the moderation role of sense of power. Our theoretical model provides a novel perspective that deepens the understanding of defensive silence and suggests implications for managerial practices.</p
A web-based tool for predicting gastric ulcers in Chinese elderly adults based on machine learning algorithms and noninvasive predictors:A national cross-sectional and cohort study
Get PDFBackground: As the Chinese population continues to age, the prevalence of gastric ulcers, a common nutrition and diet-related disorder, is rising among the elderly. Gastric ulcers pose a significant public health challenge in China, yet there is limited research to predict gastric ulcers accurately. Objective: Our study aims to employ machine learning algorithms to predict the occurrence of gastric ulcers and develop an online tool to assess the risk of gastric ulcers for elderly individuals, both currently and in the future, while identifying important predictors. Method: We used baseline data from the Chinese Longitudinal Healthy Longevity Survey in 2011 and 2014, with a follow-up endpoint of 2018. We employed nine machine learning algorithms to construct predictive models for gastric ulcers over the next seven years (2011–2018, with 1482 samples) and the next three years (2014–2018, with 2659 samples). Additionally, we utilized cross-sectional data from 2018 (with 13,775 samples) to construct a predictive model for current gastric ulcers. Results: Noninvasive predictors such as demographic, behavioral, nutritional, and physical examination factors were utilized to predict the current and future occurrence of gastric ulcers. In our study, Support Vector Machine (SVM), Random Forest (RF), and Light Gradient Boosting Machine (LGBM) achieved an accuracy of 0.97 for predicting gastric ulcers over seven years; Logistic Regression, Adaptive Boosting, SVM, RF, Gradient Boosting Machine, LGBM, and K-Nearest Neighbors reached 0.98 for three-year predictions; and SVM, Extreme Gradient Boosting, RF, and LGBM attained 0.95 for current gastric ulcer prediction. Conclusions: We developed MyGutRisk, built on optimal machine learning models, relatively accurately predicts gastric ulcer risk in elderly adults using noninvasive factors like diet and lifestyle. It supports self-assessment via a public link and clinical screening in community health settings to guide preventive measures. However, as a prototype, it requires further validation to ensure accuracy and generalizability across diverse populations and real-world applications.</p
A systematic review of multifaceted silence in social psychology
No full textThis article, conforming to the 2020 PRISMA checklist, presents a systematic review of silence within the realm of social psychology, utilizing research-driven insights. Silence can be interpreted through both interpersonal and intrapersonal lenses; that is, it can originate from external social interactions or be a personal choice. While external silence reflects responses to societal stimuli, internal silence focuses on individual decisions. The piece contends that silence possesses sociological dimensions—when an individual communicates through silence (such as expressing resistance or alienation), they not only convey personal sentiments but may also represent broader collective concerns. Drawing upon the concept of sociological imagination, it posits that what may seem like an individual issue can mirror shared societal struggles, thus highlighting how personal experiences resonate with community dynamics. By examining diverse perspectives of silence, the article elucidates its complexity and significance within social environments
The classification of platform workers in the Australian context
No full textThis article considers the issue of classification of platform workers in Australia and it is motivated by the uncertainty that persists with this issue as well as the negative impacts that may arise for workers and platforms in such an environment. We contend that awaiting a decision of the High Court, that may provide clarification on the correct classification of platform workers, is not a preferred option for resolving the issue. Introducing a third category of worker to the existing categories – employee and independent contractor – has the potential to introduce its own complexities. Another option is a legislative intervention whereby existing legislation would be amended to deem platform workers to be employees. We argue that a better alternative is for the Australian legislature to enact a legislative definition of employee similar to that in the Employment Relations Act (New Zealand) s 6(1)(a). A principle-based approach to drafting the provision, and a purposive approach to its interpretation appear to be effective means of addressing the indeterminacy that has pervaded the platform worker classification issue in Australia.</p
Young people’s perspectives on parents helping young people exposed to trauma
No full textBackground: The prevalence of trauma among young people is alarming due to its considerable effects on their wellbeing and development. Parents can provide crucial support for young people exposed to trauma, however, there is limited research on how parents can help young people exposed to trauma from a youth perspective. Objective: This study explored the perspectives of young people regarding strategies and approaches parents can take to assist young people to cope with traumatic events. Methods: An anonymous online survey created in Australia was distributed to young people aged 15 to 18 years to identify what parents can do to help young people exposed to trauma. A total of 159 young people completed the survey. Results: Qualitative thematic analysis revealed that young people felt parents could listen to and validate the experiences of young people and provide them with help and guidance. Young people recommended that parents should support those who have experience trauma by adopting a non-confrontational, empathetic, and understanding approach, and refrain from expressing anger, judgment, dismissiveness, ridicule, or blame. Young people also recommended parents encourage, empower, and provide guidance to young people exposed to trauma. Participants spoke about the importance of parents spending time with young people and ensuring that young people have access to mental health support. However, participants highlighted that parents should not pressure young people to engage in counselling. Conclusions: Implications from this study emphasise the importance of education and resources to help parents support, promote recovery and prevent further harm and re-traumatisation of young people exposed to trauma. This study has implications for mental health professionals working with parents to help them effectively support young people exposed to trauma. Results from this study inform the development of trauma-informed parenting programs to ensure that young people exposed to trauma receive adequate parental support.</p
Lung cancer detection systems applied to medical images:A state-of-the-art survey
No full textLung cancer represents a significant global health challenge, transcending demographic boundaries of age, gender, and ethnicity. Timely detection stands as a pivotal factor for enhancing both survival rates and post-diagnosis quality of life. Artificial intelligence (AI) emerges as a transformative force with the potential to substantially enhance the accuracy and efficiency of Computer-Aided Diagnosis (CAD) systems for lung cancer. Despite the burgeoning interest, a notable gap persists in the literature concerning comprehensive reviews that delve into the intricate design and architectural facets of these systems. While existing reviews furnish valuable insights into result summaries and model attributes, a glaring absence prevails in offering a reliable roadmap to guide researchers towards optimal research directions. Addressing this gap in automated lung cancer detection within medical imaging, this survey adopts a focused approach, specifically targeting innovative models tailored solely for medical image analysis. The survey endeavors to meticulously scrutinize and merge knowledge pertaining to both the architectural components and intended functionalities of these models. In adherence to PRISMA guidelines, this survey systematically incorporates and analyzes 119 original articles spanning the years 2019–2023 sourced from Scopus and WoS-indexed repositories. The survey is underpinned by three primary areas of inquiry: the application of AI within CAD systems, the intricacies of model architectural designs, and comparative analyses of the latest advancements in lung cancer detection systems. To ensure coherence and depth in analysis, the surveyed methodologies are categorically classified into seven distinct groups based on their foundational models. Furthermore, the survey conducts a rigorous review of references and discerns trend observations concerning model designs and associated tasks. Beyond synthesizing existing knowledge, this survey serves as a guide that highlights potential avenues for further research within this critical domain. By providing comprehensive insights and facilitating informed decision-making, this survey aims to contribute to the body of knowledge in the study of automated lung cancer detection and propel advancements in the field.</p
Modelling mixed-frequency time series with structural change
No full textPredictive ability of time series models is easily compromised in the presence of structural breaks, common among financial and economic variables amidst market shocks and policy regime shifts. We address this problem by estimating a semiparametric mixed-frequency model, that incorporate high frequency data either in the conditional mean or the conditional variance equation. The inclusion of high frequency data through non-parametric smoothing functions complements the low frequency data to capture possible non-linear relationships triggered by the structural change. Simulation studies indicate that in the presence of structural change, the varying frequency in the mean model provides improved in-sample fit and superior out-of-sample predictive ability relative to low frequency time series models. These hold across a broad range of simulation settings, such as varying time series lengths, nature of structural break points, and temporal dependencies. We illustrate the relative advantage of the method in predicting stock returns and foreign exchange rates in the case of the Philippines.</p
Rural South Sulawesi mothers’ emotional capital:supporting primary school children’s remote learning during COVID-19
No full textUnderstanding the impact of COVID-19 and the resulting disruptions from the shift to emergency remote learning remains an important concern, particularly for those communities where geographical, economic and social factors exacerbated the challenges. This study explores the experiences of mothers living in a rural area of South Sulawesi, Indonesia, as they encountered new challenges associated with supporting their primary school children’s education through online platforms. The study draws on in-depth interviews with 10 mothers thematically analysed through the concept of emotional capital. The findings reveal how the mothers’ own educational histories impacted on their emotive responses to helping their children with emergency remote schooling. The mothers also encountered difficulties engaging with unfamiliar technology such as smartphones that were the default technology that mediated their children’s learning. We argue that the care, time and support the mothers provided for their children’s schooling during this difficult time reflects the emotional capital they invested in their children’s education. The implication is that the emotional support that these mothers provide for their children’s learning, especially in the context of strained resources during COVID-19’s educational disruptions, should be acknowledged and valued, thus making their work less invisible.</p