Search Results (656)

The increasing integration of artificial intelligence (AI) into early childhood education has led to ethical concerns regarding children’s privacy, fairness, and developmental appropriateness. While ethical knowledge is recognised as an important component of teachers’ professional knowledge, existing instruments such as the Technological Pedagogical Content Ethical Knowledge (TPCEK) framework were developed for preservice teachers in general and do not fully capture the ethical issues specific to AI-supported preschool education. In this study, we adapted and validated a Self-Perceived Ethical Knowledge Scale for Chinese preservice preschool teachers based on the eight ethics-related dimensions of TPCEK. A two-phase sequential design was employed. In Phase 1, a three-round modified Delphi study was conducted, involving 20 experts who refined an initial 40-item pool into a 33-item scale. In Phase 2, we examined the scale’s psychometric properties by performing an exploratory factor analysis (EFA) on a pilot sample (n = 289)—with factor retention corroborated by principal-axis parallel analysis—and confirmatory factor analysis (CFA) on a separate main-study sample (n = 583). The final 31-item, eight-factor scale demonstrated satisfactory model fit (CFI = 0.943, TLI = 0.932, RMSEA = 0.047, SRMR = 0.048), internal consistency (Cronbach’s α = 0.776–0.887; CR = 0.784–0.891), convergent validity (AVE = 0.549–0.622), and discriminant validity (Fornell–Larcker criterion and HTMT < 0.85). A second-order CFA also showed acceptable fit, although it fitted the data significantly less well than the correlated first-order model. The validated scale provides a context-specific instrument for assessing self-perceived ethical knowledge among preservice preschool teachers in AI-enhanced teacher education. Full article

Algorithmic management increasingly shapes how gig work is organised, evaluated, and experienced, yet its implications for workers’ affective responses remain fragmented across the literature. This study integrates a systematic review with computational analysis to examine how algorithmically mediated work is represented in research and expressed in online gig-worker discourse. The systematic review followed PRISMA guidelines and synthesised evidence on autonomy, fairness, transparency, evaluation, resource insecurity, and worker experience. The computational component analysed a corpus of 10,000 online texts collected over 12 consecutive months in 2025 from gig-worker-related digital communities and platforms. Sentiment and emotion were examined using NLP-based classification and term extraction, with temporal and contextual patterns assessed across the corpus. The computational analysis found that negative sentiment was more prevalent than neutral and positive sentiment, with frustration, anxiety, and anger among the most prominent expressed emotions. These patterns broadly corresponded with themes identified in the systematic review, particularly concerns regarding fairness, uncertainty, autonomy, transparency, and algorithmic control. However, the temporal findings represent changes in aggregate online discourse rather than within-person emotional trajectories or causal effects. The study concludes that computational analysis provides complementary evidence of how algorithmically mediated work is discussed and affectively expressed online, while individual lived experiences, psychological outcomes, and causal mechanisms require further investigation through longitudinal and mixed-method research. Full article

The domestic pigeon (Columba livia domestica), recognized as one of the earliest species to be domesticated by humans, has sustained a profound and enduring association with human society. These birds fulfill significant social and economic roles, particularly in the context of pigeon racing competitions. To ensure fairness and authenticity in such events, as well as to address concerns related to meat fraud and adulteration, we have developed a six-color fluorescent multiplex PCR amplification system incorporating 24 short tandem repeat (STR) loci and the chromobox helicase DNA-binding gene (CHD) specific to domestic pigeons. The multiplex assay underwent comprehensive validation in accordance with the guidelines established by the Scientific Working Group on DNA Analysis Methods (SWGDAM) and the Committee on Wildlife Forensic Science Guidelines, encompassing evaluations of PCR conditions, precision, species specificity, sensitivity, stability, repeatability, and reproducibility. Furthermore, population genetic analyses and evaluations using simulated forensic case samples were conducted. The assay’s applicability to closely related species was also evaluated. Our results show that the developed pigeon STR genotyping system exhibits high levels of accuracy, specificity, repeatability, stability, and robustness. Complete genotyping profiles were obtained with only 125 pg of domestic pigeon genomic DNA. Analysis of 248 unrelated domestic pigeons obtained from a local market in Shanghai, China, yielded the cumulative probability of exclusion (CPE) and the combined power of discrimination (CPD) values of 0.999999659383865638452 and 0.99999999999999999999918448912103896, respectively. Additionally, the majority of primers effectively amplified loci in congeneric species, indicating the assay’s broad applicability. These results suggest that the multiplex system is highly polymorphic and well-suited for individual identification and kinship analysis in domestic pigeons, with potential applicability extending to related species. Full article

Artificial intelligence (AI) is moving rapidly from retrospective prediction and image analysis into treatment selection, operative planning, intraoperative guidance, and postoperative prognostication in cardiothoracic surgery. This transition raises an ethical problem that cannot be resolved by model accuracy alone: when an algorithm begins to shape a high-stakes clinical decision, the distribution of knowledge, authority, and responsibility also changes. This review synthesizes cardiothoracic and closely related medical evidence available through August 2026, with emphasis on quantitative performance, human–AI interaction, bias, patient autonomy, and liability. The available evidence is simultaneously encouraging and cautionary. Machine-learning approaches can improve predictive performance and AI-assisted thoracic planning can reduce errors and increase procedural consistency; however, these gains have not consistently translated into superior patient outcomes. Human–AI studies similarly demonstrate that improved accuracy may coexist with automation bias and overacceptance of algorithmic recommendations. Evidence of demographic performance disparities and limitations in the representativeness of training and validation datasets further raises concerns regarding fairness and equitable access to care. On this basis, we argue that cardiothoracic AI should be governed according to the level of decision influence rather than by technology type alone. We distinguish non-delegable professional duties, distributed system responsibilities, and non-transferable patient authority, and propose an Ethical Heart Team Framework for converting algorithmic output into ethically defensible clinical action. Full article

Drawing on environmental justice theory and treating social sustainability as an interpretive rather than directly measured outcome, this cross-sectional study examines perceived distributive, recognition, and procedural justice surrounding the proposed Trgovska Gora radioactive-waste facility. Data comprise 385 valid questionnaires from a field-based non-probability sample of adults in eight municipalities in Bosnia and Herzegovina. Ordinal construct validation using polychoric correlations and maximum-likelihood exploratory factor analysis provided qualified support for three primary dimensions (CFI = 0.955, TLI = 0.910, SRMR = 0.022; RMSEA = 0.128). Composite reliability ranged from 0.909 to 0.928, AVE from 0.715 to 0.763, and HTMT among the primary constructs from 0.679 to 0.845; broader governance/institutional-response items were therefore treated as supplementary. Composite means were above the neutral midpoint: distributive injustice, M = 4.304 (SD = 0.825); recognition injustice, M = 4.109 (SD = 0.920); and core procedural justice, M = 4.351 (SD = 0.744). These midpoint comparisons are descriptive rather than an independent validation of the hypotheses. HC3-robust municipality-adjusted models (N = 380) explained 21.0–28.3% of outcome variance, with municipality contributing significantly to all three models. Because the design is cross-sectional and the sample is non-probability based, the findings describe patterns among surveyed respondents rather than population-level or causal effects. The social-sustainability implications concern perceived fairness, voice, recognition, and institutional legitimacy; trust, cohesion, facility acceptance, and long-term governance capacity were not directly measured. Full article

Suicide research concerning lesbian, gay, bisexual, transgender, queer, intersex, asexual, and other sexually and gender-diverse (LGBTQIA+) populations has established a consistent pattern of inequity, but it has too often described disparity without adequately specifying how risk is produced, interrupted, or prevented. This Review revisits the evidence through a multilevel, intersectional, and prevention-oriented lens. LGBTQIA+ identities are not intrinsic causes of suicidality. Excess risk is better understood as an outcome of exposure to structural stigma, discrimination, violence, family rejection, conversion practices, socioeconomic exclusion, barriers to affirming care, and the psychological and interpersonal processes through which these conditions are embodied. At the same time, family acceptance, school connectedness, peer and community belonging, identity affirmation, inclusive policies, accessible gender-affirming and culturally responsive care, and timely evidence-based suicide interventions can alter trajectories. Yet the literature remains constrained by cross-sectional designs, inconsistent measurement, aggregation of heterogeneous populations, underrepresentation outside high-income Western settings, and a tendency to label correlates as “protective” without establishing causal or contextual validity. The next phase of the field should combine better population surveillance with longitudinal and quasi-experimental designs; distinguish ideation, self-harm, attempts, and suicide mortality; evaluate structural and service interventions; and govern digital prediction with privacy, fairness, and community accountability. The central task is no longer to document that inequities exist, but to identify which modifiable conditions reduce them, for whom, under what circumstances, and at what level of intervention. Full article

The rapid expansion of Arctic cruise tourism has intensified debates regarding sustainability, carrying capacity, and infrastructure pressure in vulnerable destinations. In response, the Icelandic government introduced an infrastructure fee on cruise ship passengers. This study examines how policymakers designed and justified the fee as a governance instrument, and what evidence would be required to establish that it functions as one. Using Iceland as a case study, the research combines qualitative document analysis of legislative and stakeholder materials with descriptive statistical data on cruise passenger flows. Policymakers framed the fee around infrastructure financing, the user-pays principle, fair contribution, and sustainable destination stewardship. However, the legislative process revealed stakeholder tensions concerning international competitiveness, regulatory predictability, and regional economic implications. Contextual data indicate a post-pandemic increase in passenger volumes relative to ship calls, highlighting the relevance of passenger-based indicators for understanding visitor intensity alongside vessel arrivals. The Icelandic case sits within a broader international trend, also visible in destinations such as Venice, Alaska, and Norway, in which authorities design and justify tourism taxation as a governance tool rather than solely as a revenue mechanism. The statutory framework does not earmark fee revenue for destination management, and an amendment adopted in 2025 reduced the rate with effect from 2026. The evidence examined establishes the fee’s governance objectives but does not demonstrate its destination-level effects. Full article

Internationally, rising income inequality has become a critical policy concern, as social disparities often translate into economic and financial vulnerabilities. While economic freedom is generally associated with market efficiency and growth, its distributional consequences remain contested. This study investigates the interplay between economic freedom, financial development, and income inequality across 26 economies from 2002 to 2024, employing panel data techniques, mainly the system generalised method of moments (GMM). In addition to the core variables, the analysis incorporates inflation, economic development, and education to control broader macroeconomic influences. The results indicate that both economic freedom and financial development exacerbate income inequality, particularly when access to financial resources is skewed toward higher-income groups. These findings contribute to the growing empirical literature on economic freedom and the finance–inequality nexus and underscore the importance of inclusive financial policies in the Global South. Policymakers are urged to design interventions that expand equitable access to financial services, thereby ensuring that economic freedom and financial development foster fair income distribution rather than reinforce existing disparities. Full article

In television dance competitions such as Dancing with the Stars (DWTS), fair and transparent outcome determination is crucial for addressing public concerns regarding contest fairness. However, existing studies face challenges due to the opacity of fan voting and the lack of a systematic evaluation of rule changes, making fairness disputes difficult to resolve. To address these challenges, this study develops a data-driven counterfactual analysis framework to quantitatively assess the impact of different voting rules on competition outcomes. We develop a constraint-based Monte Carlo framework to characterize feasible fan-vote distributions consistent with historical elimination results, together with an IS-MC variant for controlled reconstruction comparisons. A paired semi-synthetic validation comprising 1145 trials across five fan-vote-generating mechanisms, with the generated vote shares concealed during reconstruction, showed that IS-MC achieved the lowest mean MSE (0.01915) and MAE (0.08980), together with the highest mean Spearman correlation (0.382) and empirical 95% interval coverage (0.879) among the compared methods under a common sampling budget. Its lower ESS-to-valid-sample ratio nevertheless suggests a trade-off between reconstruction quality and importance-weight stability. Within the modeled counterfactual comparisons, the percentage-based rule was more sensitive to variation in fan-vote proportions, and contestants whose reconstructed fan support was high relative to their judge-score shares tended to receive more favorable modeled rankings under that rule. Under the stylized bottom-two judge-selection scenario, the modeled outcomes differed when judges retained the contestant with the higher judge score; this is a conditional sensitivity finding rather than evidence about actual judges’ decisions. Additionally, a six-feature GBDT proxy was evaluated using chronologically held-out percentage-rule observations, and SHAP analysis on the predicted-probability scale was used to characterize feature associations with modeled counterfactual reversals. We construct an end-to-end system encompassing data preprocessing, model inversion, uncertainty quantification, semi-synthetic validation, and diagnostic visualization, providing a descriptive basis for comparing DWTS voting rules. The proposed methodological framework can also be extended to other competitive scenarios relying on hybrid decision-making. Full article

Sustainability transitions depend on public willingness to carry the costs they impose, yet recognition of climate risk does not necessarily extend to support for measures that place those costs directly on households. This exploratory cross-sectional study examines that distinction among urban respondents in Adana, Mersin, and Osmaniye in Türkiye’s Mediterranean Region. The analytic dataset comprised 214 adult urban respondents. Because no sampling frame or respondent-level inclusion probabilities are available, the achieved sample is treated as an unweighted non-probability sample and no claim of population representativeness is made. Principal-axis exploratory factor analysis with oblimin rotation and parallel analysis identified three dimensions: personal climate–environmental risk, support for cost-bearing environmental action, and an exploratory environmental concern. An in-sample three-factor confirmatory sensitivity model showed acceptable fit (comparative fit index (CFI) = 0.970, Tucker–Lewis index (TLI) = 0.958, root-mean-square error of approximation (RMSEA) = 0.057, standardized root-mean-square residual (SRMR) = 0.057). Personal risk (mean (M) = 8.70) and environmental concern (M = 8.62) were much higher than cost-bearing support (M = 4.27). The within-person risk–cost support gap was 4.43 points (95% confidence interval (CI) 3.99–4.87; d_z = 1.36). Risk and concern were not significantly correlated with cost-bearing support, and their addition to a heteroskedasticity-robust (HC3) adjusted model increased explained variance by only 1.2% (p = 0.210). Provincial differences were substantial: Mersin had the widest gap, whereas Osmaniye had the highest cost-bearing support. The results do not estimate public opinion for the provinces; rather, they show that climate-policy legitimacy cannot be inferred from high risk perception alone. Policy-specific research should test fairness, effectiveness, trust, revenue use, and distributive safeguards in representative samples. For sustainability governance the implication is direct: the social pillar of sustainability—perceived fairness, affordability, and distributive protection—has to be designed into climate and environmental measures rather than assumed to follow from environmental awareness. Full article

The growing deployment of distributed renewable generation, storage, electric vehicles, heat pumps, smart buildings, and controllable demand is expanding the flexibility available to local energy systems. Energy communities provide the governance context for collective participation and value creation, while community virtual power plants provide the operational mechanism for aggregating distributed resources and connecting them to local optimisation, flexibility markets, grid services, and resilience functions. This PRISMA-ScR-informed framework development study synthesises the literature from Scopus, Web of Science, and IEEE Xplore to examine how artificial intelligence supports community VPP operation, where agentic AI adds capabilities beyond established optimisation, reinforcement learning, and multi-agent systems, and which design requirements follow governed orchestration. The synthesis shows that current evidence is strongest for component-level forecasting, scheduling, bidding, adaptive control, distributed coordination, and digital-twin validation, whereas integrated agentic orchestration remains an emerging direction. Classical multi-agent systems already provide decentralised representation, communication, negotiation, and coordinated control; the additional role proposed for agentic AI is therefore narrower and concerns context-aware multi-step workflow orchestration, governed tool use, exception handling, grounded explanation, and bounded delegation across existing analytical and control services. The study introduces operational flexibility intelligence as the capability to transform potential distributed flexibility into deployable, authorised, market-, grid-, resilience-, and community-compatible action. It further develops a conceptual layered reference architecture in which agentic orchestration operates through governed tools and interfaces rather than bypassing validated resource controllers. Fairness, comfort, privacy, cybersecurity, resilience, auditability, and human-in-command authority are treated as cross-cutting operational constraints. The architecture defines a design and validation agenda rather than an empirically validated implementation. Full article

Background: Across jurisdictions, forensic psychiatry evaluations primarily rely on clinical assessment, psychometric testing, and historical and behavioural information. Following advances in neuroscience and neurolaw, there is a growing interest in incorporating biological markers (such as structural and functional neuroimaging (structural MRI, CT, fMRI), electrophysiology (EEG)) into forensic psychiatric reports. Empirical studies indicate that courts are increasingly encountering such data, particularly in cases of homicide, severe violence, insanity evaluations, and capital sentencing. Methods/Results: Neuroimaging can document brain disease or injury that may impair cognition, affect regulation, or impulse control, important to assessment of criminal accountability, and future risk. Biological markers may corroborate psychiatric and neurological diagnoses, and as such make expert reasoning more transparent. Assessments that omit biological investigations may be considered incomplete, with a risk of undermining fair judicial process and equality before the law, as well as hampering future treatment and risk management. However, there are notable challenges that limit the use of biomarkers in forensic psychiatry evaluations. Neuroscientific findings have methodological and inferential limitations affecting their practical use in individual cases. Furthermore, there is a mismatch between the empirical nature of neuroscience and the normative legal questions such as those of criminal accountability and intent. Additional concerns include risks of over-interpretation (“brain overclaim”), “double-edged sword” effects (mitigation versus inferred dangerousness), inequitable access, and the absence of robust, jurisdiction-specific standards. Conclusions: The inclusion of biological markers in forensic psychiatry evaluations has considerable potential benefits but also raises challenges that need to be addressed in future practice, research, and guideline development. Full article

Intelligent Tutoring Systems (ITS) can provide adaptive explanations, practice, feedback, and learner modelling at a scale that conventional one-to-one tutoring cannot readily achieve. Yet learning gains alone do not establish that an ITS is sustainable or beneficial to social well-being. This critical review integrates evidence on ITS effectiveness with research and policy guidance concerning learner agency, psychological well-being, inclusion, accessibility, data governance, algorithmic fairness, teacher roles, institutional capacity, and environmental responsibility. The review identifies a persistent evaluation gap: many studies privilege short-term achievement while under-measuring distributional effects, autonomy, emotional safety, teacher workload, lifecycle cost, and computational impact. To address this gap, the paper proposes the Human-Centered Sustainable Intelligent Tutoring Systems (H-SITS) framework as a conceptual and integrative framework for organizing candidate dimensions of sustainable tutoring and guiding future empirical validation. The framework organizes seven interdependent dimensions: pedagogical effectiveness; learner agency and well-being; inclusion and accessibility; trust, privacy, and fairness; teacher augmentation; institutional and economic viability; and environmental responsibility. It also presents a staged implementation roadmap and a research agenda emphasizing longitudinal, participatory, and equity-sensitive evaluation. The central conclusion is that sustainable tutoring is not a property of an algorithm alone. It is an outcome of socio-technical design, governance, and continuous evidence-based adaptation that keeps human flourishing, educational justice, and public value as primary objectives. Full article

Background: Instrumental shade determination may reduce some sources of subjectivity associated with visual shade selection, but device repeatability and interchangeability remain clinically relevant concerns. Objective: The aim of this study was to evaluate the reproducibility and clinical utility of two spectrophotometric devices (Easyshade and Rayplicker Cobra) for tooth shade measurement and selection. Methods: In total, 35 adults were evaluated at seven predefined maxillary tooth regions: the cervical, middle, and incisal thirds of teeth 2.1 and 2.3, and the middle third of tooth 2.6. Each instrumental measurement was repeated three times, with complete removal and repositioning of the device between readings. Visual shade selection using the VITA Classical guide was performed once per site by the same trained operator, whose normal colour vision had been confirmed before data collection. Teeth were kept hydrated, patients remained in the same position, and both devices were calibrated according to the manufacturers’ instructions. Intra-device strict repeatability and inter-method agreement were assessed descriptively and using Cohen’s Kappa, as appropriate. Results: Descriptive pooled strict repeatability was 52.2% for Easyshade and 53.1% for Rayplicker Cobra. Repeatability varied by tooth and region. Inter-device agreement was predominantly slight to fair (0.053–0.386). Easyshade showed moderate-to-substantial agreement with the visual comparator in selected regions, with the highest Kappa in the middle third of tooth 2.1 (0.619), whereas Rayplicker Cobra did not exceed fair agreement (maximum 0.362). Conclusions: Both spectrophotometers showed similar descriptive strict repeatability percentages; however, their low inter-device agreement indicates that their categorical shade outputs should not be considered interchangeable. The greater agreement of Easyshade with VITA Classical in selected regions reflects inter-method comparability rather than superior trueness, as no independent instrumental reference standard was used. Full article

Fairness in recommendation systems has drawn growing attention due to rising societal and regulatory concerns over algorithmic bias. Existing fairness-aware approaches typically mitigate bias by either removing sensitive attributes via representation learning or leveraging causal-path interventions (e.g., counterfactual or specific-path debiasing) to distinguish genuine causal effects from confounder-induced correlations between sensitive attributes and user preferences. However, when it comes to evaluation, most prior work adopts both Demographic Parity (DP) and Equal Opportunity (EO) as simultaneous criteria, yet overlooks their inherent tension and the causal nature of the sensitive attribute. Specifically, if a sensitive attribute genuinely drives preference variation, enforcing DP forces equal exposure across groups, contradicting natural interest diversity and severely hurting accuracy; conversely, for spurious correlations, relying solely on EO fails to remove confounder-introduced bias. More importantly, these metrics are typically computed in a static, one-shot manner, ignoring that recommendation is an iterative process where even minor initial disparities can be amplified over time through feedback loops, eventually leading to substantial long-term unfairness. Nevertheless, existing studies rarely address such dynamic, long-term fairness implications, leaving a critical gap in both evaluation and optimization. To resolve this, we propose Long-term Fairness-aware Recommendation via Adaptive Fairness Metric Selection (LFR-via-AFMS). Our framework first learns the causal structure to identify whether the sensitive attribute has a genuine causal effect or merely a spurious association with user preferences. Based on this diagnosis, it adaptively selects the most appropriate fairness criterion: Equal Opportunity for true causality, which allows legitimate group differences in preference, and Demographic Parity for spurious correlations, which eliminates unjustified disparities entirely. The adaptively chosen metric is then integrated into an actor–critic reinforcement learning reward to optimize long-term fairness without sacrificing accuracy. Extensive experiments on Alibaba and MovieLens datasets, with five independent runs and statistical significance testing, demonstrate that the proposed method achieves a superior fairness-accuracy trade-off compared with state-of-the-art baselines, and the adaptive metric selection proves indispensable for maintaining both equity and recommendation quality. We validate the causal diagnosis module through simulation studies with known ground truth and sensitivity analyses confirming robustness across threshold choices. Full article