Search Results (528)

Urban accessibility is commonly evaluated using static spatial indicators, which assume stable travel conditions throughout the day. Road congestion, network saturation, and service variability change the function and experience of the built environment (BE). This study tests the Temporal City Framework (TCF) by examining how time of day (TOD) reshapes urban accessibility and travel behaviour with varying levels of congestion. Using 30,288 trip records from the 2022 US National Household Travel Survey (NHTS), duration is operationalised as a sixth dimension of the BE. A time-normalised impedance metric, measured in minutes per mile (MPM), is used that captures realised congestion independently of distance. Temporal impedance (TI) varies strongly with TOD, with substantially higher MPM during peak and midday periods than at night. Compared with nighttime conditions, midday travel requires approximately 19% more time per mile. This indicates a measurable contraction in functional accessibility under identical BE conditions. The TI model outperforms duration-only models, with impedance remaining dominant when both measures are included. These results support interpreting duration as a structural dimension of urban accessibility. TI significantly increases the relative likelihood of active and public transport compared to private cars, even after accounting for absolute trip duration. Hired transport modes (taxi and ride-hailing services) are most prevalent at night, reflecting a greater reliance on on-demand services outside regular daytime schedules. This study tests duration as a structural dimension of the BE by operationalising time-normalised TI. Associations are interpreted as trip-level behavioural constraints rather than causal effects. Planning frameworks based on static travel times systematically misrepresent exposure, equity, and travel mode feasibility. Time-stratified accessibility metrics should therefore be integrated into transport and land-use evaluation and associated policies. Full article

Student evaluations of teaching (SETs) play a central role in hiring, promotion, and retention decisions in higher education; however, research indicates that they may be influenced by perceptions about instructor identity rather than teaching effectiveness. Guided by role congruity theory, which suggests that gendered expectations influence judgments when individuals occupy roles historically associated with another sex or gender, this study examines how students’ written comments reflect stereotypes, notably those related to gender. Using qualitative analysis of narrative SET responses, we identify recurring linguistic patterns that reveal how gender intersects in shaping perceptions of (Black) cisgender faculty. Results from the study show that women instructors were frequently described in relational and mentorship-oriented language, whereas men instructors were framed in terms of authority, rigor, and intellectual challenge. While both groups received overall positive evaluations, these differentiated descriptors highlight subtle mechanisms through which bias can operate and reinforce normative expectations. We also consider our positionality as cisgender scholars and reflect on the broader cultural and institutional contexts that inform evaluations of teaching, underscoring the need for equitable and reflective evaluation practices to further educational equalities in higher education, including the disruption of cisnormativity. Full article

This study investigates the typology of the pastoral farming systems in the arid region of southern Tunisia, with a particular focus on the governorate of Tataouine. A field survey was conducted among 111 livestock farmers distributed across different agro-ecological zones. The typology of breeding systems was established using a Factor Analysis of Mixed Data (FAMD), which identified eleven dimensions explaining 69.74% of the total data variance. The first three dimensions accounted for 15.91%, 8.79%, and 7.67% of the variability, respectively, and were defined by herd composition, resource availability, and management strategies, including variables such as the number of goats, sheep, and camels, distance to water sources, infrastructure, reproductive practices, and workforce availability. Hierarchical clustering revealed three distinct systems: System 1, regrouping “Small Urban Farmers”, defined by small-scale operations relying on family labor, localized feed resources, and market-driven production targeting urban consumers; System 2, representing large livestock, composed of professionalized operations with improved infrastructure, hired labor, and transhumance practices to optimize resource use and productivity; and System 3, for herds with camels, characterized by extensive systems utilizing collective rangelands and camels to adapt to arid conditions and ensure ecological resilience. The results emphasize how ecological constraints, infrastructure, and spatial organization shape the diversity of these systems. This typology provides critical insights into the challenges and potential of livestock farming in arid environments and offers a foundation for designing targeted interventions to support the sustainability of pastoral systems under increasing environmental and economic pressures. Full article

Many universities acknowledge a responsibility to address climate change and are actively working to meet this goal in academic programs and undergraduate curricula. This paper provides insights from interviews with university leaders from 20 American and Canadian institutions pursuing climate action via education. Interviewees described a range of initiatives, including new General Education requirements (GEs), cross-disciplinary courses, domain-specific classes, and certificate programs, as well as the establishment of dedicated climate schools. Pathways for curricular change include academic senate climate committees, top-down support from university leadership, bottom-up advocacy and activism from faculty and students, and opportunities to leverage evolving systems. To increase climate-teaching capacity, interviewees reported instituting team teaching, supporting faculty learning opportunities, hiring faculty with climate expertise, and partnering with organizations outside academia. Qualitative data collected during these interviews were thematically coded, revealing significant takeaways including the need to appropriately reward faculty for climate-teaching efforts and to recognize the complementary virtues of high-level courses like GEs with broad reach versus deeper dives for climate-related majors with targeted reach. This paper synthesizes advice from educators who succeeded in increasing climate education at their institutions and concludes with suggestions on how to integrate climate more fully into academia’s educational mission. Full article

Social media has become a key venue where PhD graduates seek career information, compare experiences, and negotiate uncertainty. Drawing on information behavior and sense-making perspectives, this study examines how returnee PhDs from non-core study destinations discuss employment challenges in China’s academic labor market when credential signals are contested. Using Korean-trained PhDs as a theoretically motivated exemplary case, we collected 1149 publicly available posts from Xiaohongshu, a Chinese social media platform, and applied BERTopic to identify latent themes, followed by qualitative close reading of representative posts to interpret discourse functions. The model yielded ten topics, and semantic association analysis indicates substantial overlap among high-frequency topics, suggesting intertwined concerns rather than neatly separated issue domains. The four most prevalent topics account for 72.06% of the corpus, centering on credential recognition, job-search pathways, informal screening rules, and intersecting age- and gender-related pressures. Qualitative readings further reveal recurring discursive moves, including exposing tacit hiring heuristics, contesting stigmatizing labels (e.g., “water PhD,” a derogatory term implying low-quality credentials), and exchanging actionable strategies across regions and career tracks. Overall, the findings point to discursive convergence under evaluation uncertainty: when formal criteria are ambiguous and institutional signals are unreliable, participants turn to social media to stabilize expectations by triangulating cases and iteratively refining shared interpretations of the job market. This study contributes empirical evidence on uncertainty-driven information practices in highly educated labor markets and demonstrates the value of combining topic modeling with qualitative interpretation to capture online collective sense-making. Full article

This study examined the policies governing the teaching workforce in China’s higher vocational education system. We developed a two-dimensional analytical framework (“policy content elements–policy tools”) to conduct an in-depth analysis of 124 central and provincial policy texts. The key findings are as follows: (1) Imbalance in policy tools: Authoritative and capacity-building tools dominate, while symbolic and exhortative tools are underutilized. Disparities exist between the central and provincial policies regarding the deployment of specific tools. (2) Prioritization of content elements: The strongest emphasis is placed on teacher cultivation, followed by teacher evaluation and safeguarding. Policies concerning teacher recruitment (access) have received little attention. (3) Policy misalignment: Poor coordination between policy tools and content elements undermines overall policy effectiveness. To address these issues, we propose the following: (1) Optimizing the policy tool portfolios: Reduce overreliance on authoritative tools for teacher recruitment and strengthen the use of incentive-based and capacity-building tools for evaluation and safeguards. (2) Strengthening recruitment policies: Formalize qualification standards, rigorously enforce teaching certifications, and standardize hiring procedures. (3) Enhancing policy coordination: Incorporating regional variations to improve the evidence-based integration of policy tools. These recommendations aim to refine the teaching workforce policies and advance the high-quality development in higher vocational education. Full article

Teacher shortages and workforce trends in education are critical policy and research concerns. This study presents a robust data collection pipeline that systematically web-scrapes job postings for K-12 and higher education job postings across multiple sources. While the methodology could theoretically be adapted to other job categories, the pipeline is specifically implemented for educational job postings due to platform-specific structures and scraping constraints. Using R, we extract, clean, and archive job postings weekly, compiling them into a longitudinal master dataset that tracks trends in teacher openings over time. Our approach enables monthly trend analysis, providing insights into hiring patterns, subject-area demands, and geographic disparities. By making this dataset available, we contribute both a reproducible methodological pipeline for scraping, cleaning, and standardizing K-12 and higher education job postings, and a validated longitudinal dataset for research and workforce policy applications. This data descriptor details the methodology, data structure, and potential applications for researchers and policymakers monitoring education sector employment trends. Full article

Employee turnover presents a significant challenge to modern organizations, often resulting in operational disruptions, substantial hiring costs, and a loss of institutional knowledge. While traditional human resource practices have historically been reactive, the emergence of machine learning has introduced a proactive capability to anticipate and mitigate attrition before it occurs. This research utilizes the IBM HR Analytics dataset, which contains 1470 employee records and 35 distinct features, to develop a hybrid machine learning model designed to enhance the accuracy of turnover predictions. To ensure the model’s effectiveness, the researchers employed a comprehensive preprocessing phase that included eliminating non-informative features, applying label encoding to categorical data, and using StandardScaler to normalize quantitative values. A critical component of the study addressed the common issue of class imbalance within HR data. To resolve this, a hybrid sampling strategy was implemented, combining Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN) to create a more balanced learning environment for the algorithms. The core of the predictive engine is a soft voting ensemble that integrates three powerful algorithms: Random Forest, XGBoost, and logistic regression. Evaluated on an 80/20 train–test split, the tuned XGBoost model achieved an impressive 84% accuracy and an Area Under the Curve (AUC) of 0.80. Meanwhile, the logistic regression component contributed the highest F1-score, reinforcing the overall strength and balance of the ensemble approach. These metrics confirm that the hybrid model is both robust and reliable for identifying at-risk employees. Beyond simple prediction, the study prioritized interpretability by using SHapley Additive exPlanations (SHAP) to identify the primary drivers of attrition. The analysis revealed that the most significant variables influencing an employee’s decision to leave include the interaction between job level and experience, frequent overtime, monthly income, current job level, and total years spent at the company. By providing these data-driven insights, the model empowers HR teams to transition from reactive troubleshooting to proactive retention planning, ultimately securing the organization’s talent and stability. Full article

Background/Objectives: The aim of this study was to synthesise qualitative evidence from family members’ experiences of long-term home care for older adults provided by live-in migrant caregivers. Methods: We conducted a systematic literature review with meta-synthesis using four online databases. The search included articles published between January 2016 and December 2025 on the CINAHL, PubMed, SCOPUS and WOS databases. Thematic synthesis of qualitative data was conducted. Results: Eleven papers from six different countries fulfilled the criteria and were included in the thematic synthesis. Four main themes were identified: 1. Not an easy decision. 2. A stranger at the heart of family life. 3. Two worlds that meet and need each other. 4. Improving the integration of migrant caregivers into family life. Hiring migrant caregivers to provide long-term home care for older adults can ease the burden on family caregivers, but it is an additional source of stress and worry. Conclusions: The family members of older adults call for greater financial and institutional support, as well as the involvement of social and health services in the training and education of families and migrant caregivers. Negotiation skills and the ability to reach consensus between older adults (OAs), family members and resident migrant caregivers are key to improving cohabitation and care for OAs. The primary goal is the well-being of the OAs, which involves overcoming cultural prejudices, learning together in response to the new situation, improving caregivers’ training, and ensuring continuity of care. Full article

This study explores the intersection of social advocacy and commercial brand endorsements, with a particular focus on the role of gender in shaping these dynamics. Drawing from Social Role Theory, the study examines how male and female social media influencers engage in advocacy, their motivations, and the strategies they employ when balancing activism with commercial interests. Using qualitative methods, in-depth semi-structured interviews were conducted with 20 social media influencers (10 male, 10 female) recruited from diverse geographic regions across Africa, Asia, the Americas, and Europe. A reflexive thematic analysis of the interview data reveals significant gender differences in advocacy approaches. Female influencers tend to engage in social causes with a strong relational and emotional investment. In contrast, male influencers approach advocacy with cautious engagement, often prioritizing objectivity and risk management. In examining the intersection of brand endorsements and advocacy, the study finds that female influencers emphasize ethical consistency and audience trust, aligning brand partnerships with their social values. Male influencers, on the other hand, view advocacy as a strategic asset that enhances brand reputation while maintaining professional neutrality. The research also offers practical implications for brands considering hiring influencers who engage in social cause advocacy. Full article

China is the world’s largest pear producer, yet its production remains constrained by structural inefficiencies and regional disparities. Clarifying the spatiotemporal evolution of pear production land and its driving mechanisms is essential for improving efficiency and supporting sustainable agricultural development. Using provincial panel data from 29 Chinese regions during 2001–2020, this study analyzes changes in pear yield, planting area, and yield per unit area by integrating the Production Concentration Index, Exploratory Spatial Data Analysis, and Comparative Advantage Analysis. A Spatial Durbin Model is applied to quantify both direct and spatial spillover effects of natural conditions, opportunity costs, infrastructure, technology, market demand, and policy. The results indicate a shift in pear production from area-driven expansion to efficiency-oriented growth, alongside a gradual westward relocation and declining spatial dependence. While core producing regions remain dominant, several western regions have enhanced their comparative advantages. Labor-related factors are crucial: expanding non-agricultural employment opportunities constrain pear production (−0.482), but agricultural mechanization indirectly increases rural labor hiring costs (0.089), whereas agricultural mechanization (0.144) and moderate increases in labor costs (0.126) contribute positively to regional production efficiency. Improved transportation infrastructure, irrigation, fertilizer input, market demand, and policy further promote pear production, with evident spatial spillover effects. These research findings provide empirical support for optimizing regional pear production layouts and formulating applicable policies. Full article

Employment growth is a central objective of economic policy, yet the role of internal market integration in shaping labor demand remains understudied. This paper examines the impact of the Unified National Market initiative on firm-level employment, utilizing a panel of Chinese listed companies from 2011 to 2023. Our estimates reveal a robust positive relationship between market integration and labor hiring. This impact is heterogeneous, with stronger responses observed in non-state-owned enterprises, labor-intensive sectors, and the more developed eastern region. We provide evidence for two distinct mechanisms: a production scale effect resulting from expanded market access, and a credit channel driven by the relaxation of financing constraints. Additionally, we document that market integration improves the skill composition of employment and increases the labor share of income. These results underscore the importance of reducing inter-regional barriers to achieve employment growth. Full article

Social organizations increasingly rely on Natural Language Processing (NLP) to analyze large-scale textual data for high-stakes decisions, including university admissions, financial aid allocation, and job hiring. Current methods primarily employ topic modeling and sentiment analysis, but they fail to capture the complex ways emotions and cultural contexts shape meaning in text, potentially perpetuating bias and undermining equitable decision-making. To address this gap, we introduce the Behavioral and Emotional Theme Detection (BET) framework, a novel approach that integrates emotional, cultural, and sociological dimensions into topic detection and emotion analysis. By applying BET to English and Hebrew datasets, we showcase its multilingual adaptability and its potential to reveal rich thematic content and emotional resonance in biographical texts. Our results demonstrate that BET not only enhances the granularity and diversity of detected themes but also tracks shifts in emotional framing over time, offering deeper insights into how individuals deploy linguistic resources to position their identities, enabling more equitable assessment practices. Full article

Workplace weight stigma is a form of discrimination affecting equality, health, and careers, yet occupational research remains fragmented. This gender-sensitive systematic review synthesizes evidence on workplace weight stigma among adult workers and job applicants since 2000. Following PRISMA procedures, we searched psychological, medical, sociological, and economic databases, identifying 25 included studies examining work outcomes. The corpus includes experimental vignette and correspondence studies, surveys, and qualitative designs, predominantly from high-income Western countries. Higher body weight is consistently associated with disadvantages across the employment life cycle: reduced callbacks and hiring, lower wages and wage growth, fewer promotions, and negative performance evaluations. Penalties are systematically stronger for women; intersectional analyses remain rare. Weight-based teasing, unfair treatment, and stereotype threat are linked to poorer self-rated health, psychological distress, burnout, reduced work ability, lower job satisfaction and commitment, and stronger turnover intentions. Organizational-level evidence is indirect but suggests detrimental effects on engagement and citizenship behaviors. Findings support conceptualizing workplace weight stigma as both a psychosocial hazard and a structural driver of labor-market inequality, underscoring the need for size-inclusive HR practices, leadership, and occupational risk-prevention policies. Full article

As artificial intelligence systems proliferate across critical societal domains, understanding the nature, patterns, and evolution of AI-related harms has become essential for effective governance. Despite growing incident repositories, systematic computational analysis of AI incident discourse remains limited, with prior research constrained by small samples, single-method approaches, and absence of temporal analysis spanning major capability advances. This study addresses these gaps through a comprehensive multi-method text analysis of 3494 AI incident records from the OECD AI Policy Observatory, spanning January 2014 through October 2024. Six complementary analytical approaches were applied: Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF) topic modeling to discover thematic structures; K-Means and BERTopic clustering for pattern identification; VADER sentiment analysis for emotional framing assessment; and LIWC psycholinguistic profiling for cognitive and communicative dimension analysis. Cross-method comparison quantified categorization robustness across all four clustering and topic modeling approaches. Key findings reveal dramatic temporal shifts and systematic risk patterns. Incident reporting increased 4.6-fold following ChatGPT’s (5.2) November 2022 release (from 12.0 to 95.9 monthly incidents), accompanied by vocabulary transformation from embodied AI terminology (facial recognition, autonomous vehicles) toward generative AI discourse (ChatGPT, hallucination, jailbreak). Six robust thematic categories emerged consistently across methods: autonomous vehicles (84–89% cross-method alignment), facial recognition (66–68%), deepfakes, ChatGPT/generative AI, social media platforms, and algorithmic bias. Risk concentration is pronounced: 49.7% of incidents fall within two harm categories (system safety 29.1%, physical harms 20.6%); private sector actors account for 70.3%; and 48% occur in the United States. Sentiment analysis reveals physical safety incidents receive notably negative framing (autonomous vehicles: −0.077; child safety: −0.326), while policy and generative AI coverage trend positive (+0.586 to +0.633). These findings have direct governance implications. The thematic concentration supports sector-specific regulatory frameworks—mandatory audit trails for hiring algorithms, simulation testing for autonomous vehicles, transparency requirements for recommender systems, accuracy standards for facial recognition, and output labeling for generative AI. Cross-method validation demonstrates which incident categories are robust enough for standardized regulatory classification versus those requiring context-dependent treatment. The rapid emergence of generative AI incidents underscores the need for governance mechanisms responsive to capability advances within months rather than years. Full article