Search Results (1,762)

Disability language use shifts with changing social attitudes. While person-first language (PFL) emphasizes humanity, identity-first language (IFL) depicts disability as integral to one’s identity. This study of 238 participants aged 18–30 investigated language preferences and whether language type influences perceptions of identity centrality. Participants rated the importance of identity markers to a PFL- or IFL-framed subject and indicated whether PFL or IFL are preferable in conversation. Results show that perceptions are influenced by identity type (i.e., disability versus non-disability), but not by whether identity is framed with PFL or IFL. PFL is preferred for most disabilities, except for sensory disabilities, for which, along with non-disability-related identities (e.g., race), IFL is preferred. These findings suggest that, despite current social trends favouring PFL, PFL versus IFL framing may have little effect on perceptions of people with disability. Dispositional factors were not found to be strong predictors of language preference, suggesting that disability language and the conceptualization of disability may be shaped by social factors rather than by internal states. Thus, considering the preferences and experiences of disability communities when referencing disability may foster more meaningful change in how disability is conceptualized. Full article

Human anatomy teaching is undergoing a rapid transformation driven by digital innovation, immersive visualization, open multiscale datasets, and changing learner profiles, while donor-based dissection nevertheless remains essential for understanding human anatomical variability and tissue properties and may contribute to students’ professional identity formation when supported by structured reflection. In this narrative review, we synthesize peer-reviewed literature, prioritizing contributions most relevant to current educational practice and governance. We discuss how technology-enhanced resources can extend learning before, during, and after laboratory sessions through manipulable visualization, repetition, and tighter links to imaging and data literacy. We also examine the expanding clinical, research, and innovation roles of donor-based anatomy laboratories and the associated need for explicit governance (biosafety, access control, documentation, and transparency regarding secondary uses). Ethical literacy is treated as a core learning outcome, including inclusive representation, consent and donation law awareness, and critical engagement with plastination and public display. Finally, we consider how attention fragmentation and AI-enabled tools may support personalization and feedback, yet also introduce risks such as automation bias and superficial recognition if not paired with verification and reasoning-based tasks. Overall, the future of human anatomy education lies in purposeful integration of donor-based, digital, immersive, and computational modalities within an adaptive learning ecosystem that serves learners across disciplines while maintaining robust ethical and professional standards. Full article

Offline handwriting functions as a practical biometric sensing modality for identity verification and controlled stress testing of recognition pipelines, yet recovering writer-specific stroke appearance from only a few enrollment samples remains difficult. In trajectory-based rendering used to probe such sensing systems, stroke width is often fixed or only weakly adapted, so the sensed thickness cue fails to reflect individual writing habits. The key obstacle is an operational reliability imbalance: a learned width estimator and handcrafted geometric priors disagree in trustworthiness across writers and even along a single stroke, which makes a constant fusion rule brittle for few-shot personalization. We propose a confidence-guided dynamic fusion framework whose primary method is a deterministic confidence-to-$\beta$ map (not a learned gate): point-wise pseudo-label confidence from reference images sets per-point blending weights between learned and rule-based widths, with “reliability” denoting this operational blending prior. On 73 HWDB writers under a strict writer-disjoint protocol with 240 adaptation runs, dynamic fusion reduces writer-level variance and cross-seed instability relative to fixed fusion while retaining competitive point-wise accuracy. A security-oriented sensing analysis further contrasts detector responses under generic versus writer-adapted rendering, showing that the framework can serve as a controllable probe of verification sensitivity. The method plugs into existing trajectory generators and supports reproducible evaluation of personalization fidelity and biometric sensing robustness. Full article

Rural LGBTQ+ college students remain understudied in higher education. This study provides one of the few large-scale, multi-institutional quantitative examinations of this student population. Using 2023–2025 National Survey of Student Engagement (NSSE) data from nearly 300,000 first-year and senior students across 872 four-year institutions in the U.S., we combined variable-centered regression analyses and person-centered cluster analyses to examine engaging interactions with others and engaging institutional environments. We found distinct patterns of engagement associated with rural background, LGBTQ+ identity, and their intersection. Rural students reported less frequent engagement in some interactions with others but generally had more positive perceptions of their environment. LGBTQ+ students, however, had more frequent engaging interactions with others but a less favorable view of their environment. The interaction between rural and LGBTQ+ identities was positive across all engagement outcomes. The person-centered analyses further emphasize the non-monolithic nature of rural LGBTQ+ experiences. Interpreted through an asset-based perspective informed by queer(ed) rural cultural wealth, these findings complicate prevailing deficit narratives while identifying the need for further research on the unique experiences and assets of rural LGBTQ+ college students. The study demonstrates the value of more nuanced, critical approaches to rural higher education research, policy, and institutional practice. Full article

Existing cross-modal person re-identification datasets still lack full-scale benchmarks for sketch-to-RGB person retrieval. To address this problem, this paper investigates sketch-to-RGB cross-modal person re-identification. First, this paper constructs a full-scale Sketch-Market1501 dataset based on Market-1501, containing 16,304 generated sketch-style images, including 12,936 images corresponding to the training subset and 3368 query images, and covering all 1501 person identities in Market-1501. By retaining the original annotations and official data partitions, a standardized sketch-to-RGB retrieval protocol is established, in which 3368 generated sketch-style images serve as queries and 15,913 original real-world RGB images form the gallery. Using OSNet-AIN, which incorporates style normalization, as the baseline, this paper systematically investigates the effect of different training-sample mixing ratios. The experimental results show that $p=0.75$ yields a Rank-1 result of 61.2% and is subsequently used in the attention-module and loss-function studies. At the high-level feature stage, an Efficient Channel Attention (ECA) module is embedded to recalibrate high-level channel responses and improve cross-modal feature discrimination. Furthermore, a joint loss combining Softmax loss, Triplet loss, and Focal loss is adopted to optimize feature discriminability. The experimental results indicate that the constructed dataset supports sketch-to-RGB cross-modal training. The final model achieves 43.3% mAP and 63.0% Rank-1, representing improvements of 3.6 percentage points in mAP and 1.8 percentage points in Rank-1 over the OSNet-AIN baseline under the same $p=0.75$ setting, thereby providing a benchmark and reproducible baseline for future research on sketch-based person re-identification. The proposed Sketch-Market1501 dataset will be publicly available after publication of the paper. Full article

As religious diversity increases, identifying contexts that foster positive relationships across faith traditions has become increasingly important. Many interfaith initiatives emphasize dialogue and religious literacy, yet these largely cognitive approaches can inadvertently foreground the doctrinal differences they hope to bridge. Drawing on perspectives of lived religion, embodied spirituality, and intergroup contact theory, we argue that sport—an embodied, cooperative practice shared across cultures and traditions—offers a distinctive context for interfaith formation, one in which relationships emerge through shared bodily activity and common purpose rather than doctrinal negotiation. This qualitative study examined mentors’ experiences in Y-Sport: Greater Purpose and Performance, an interfaith sports leadership program developed through a partnership between Brigham Young University and Baylor University. Although both communities profess faith in Jesus Christ, we use interfaith—consistent with the field of interfaith studies—to denote engagement across two distinct, historically estranged religious communities, without prejudging either community’s Christian identity. Eleven Protestant and Latter-day Saint mentors completed semi-structured interviews analyzed using the team-based coding approach developed by Dollahite and Marks in the American Families of Faith project. Four themes emerged: (1) discovering common ground through shared purpose, (2) developing relationships marked by love, belonging, and friendship, (3) appreciating religious differences without perceiving them as threatening, and (4) experiencing personal and spiritual transformation. Participants consistently reported that their shared commitment to serving youth and strengthening faith in Jesus Christ became more salient than doctrinal differences, fostering authentic friendships while preserving distinct religious identities. Consistent with intergroup contact theory, these findings suggest that embodied cooperation organized around a shared purpose may cultivate meaningful interfaith friendships, with implications for interfaith education, mentoring, and peacebuilding. Full article

Customer data platforms (CDPs) are increasingly positioned as the data and decision infrastructure of artificial intelligence (AI)-driven marketing. Yet the academic literature has not adequately explained when a CDP can reconcile privacy compliance with hyper-personalization, nor how this reconciliation depends on platform governance, consumer trust, data quality, and responsible AI controls. This article develops an integrative conceptual framework through a structured interdisciplinary literature review. The review covers marketing, information systems, platform studies, consumer behavior, data governance, privacy engineering, and AI ethics. A structured integrative review with a systematic search component and concept-centric synthesis produced a final analytical corpus of 70 sources, comprising 60 peer-reviewed publications, one book, eight authoritative legal, policy, or standards documents, and one industry source used only for descriptive context. The synthesis identifies CDP governance capabilities as the central antecedent of two complementary mechanisms: privacy assurance and customer-data quality. Privacy assurance strengthens consumer trust and willingness to disclose first- and zero-party data, while governed identity resolution, provenance, interoperability, and data minimization improve the reliability of inputs available to machine learning, recommendation systems, generative AI, and autonomous agents. The resulting personalization effectiveness influences customer and marketing outcomes, but the relationships are conditional on responsible AI controls, platform ecosystem governance, regulatory intensity, data sensitivity, consumer privacy orientation, and organizational maturity. Seven direct-effect propositions and a decomposed family of path-specific moderation and boundary propositions specify theoretically expected relationships without implying causal identification by the review itself. The article contributes by conceptualizing the CDP not as a neutral database or stand-alone compliance tool, but as a governed platform control plane that coordinates data rights, AI decisioning, and multi-actor accountability. The framework offers a research agenda and practical design principles for privacy-preserving, transparent, and accountable AI-driven marketing ecosystems. Full article

Medical gaslighting occurs when clinicians undermine patients’ first-person authority to make claims about their own experiences, leading patients to doubt their own perceptions, beliefs, motivations, and even their sanity. This concept has proven useful for individuals to understand, convey, and find support in the aftermath of a specific type of harmful experience. However, there is much more that remains to be done in terms of clarifying and changing structural conditions that enable medical gaslighting. Elizabeth Barnes identifies many factors that compromise the quality of health care patients with marginalized identities receive without providing a method for describing how biases at the level of medical institutions and professionals enable medical gaslighting. Barnes is concerned that calls to “trust patients” as a remedy for medical gaslighting will lead physicians to grant patients too much credibility. She develops an alternative that addresses the question—under what conditions should physicians trust patients? I am concerned with a different question—under what conditions should patients trust physicians? Critical phenomenology provides a method both for describing individuals’ lived experiences of medical gaslighting and avoidable misdiagnoses. It also elucidates how structural conditions make both possible and more likely for patents who are members of marginalized groups, especially when there is medical uncertainty. These are not simply failures of competence among individual physicians; they stem from systemic biases in medical education and norms of clinical practice. Full article

Introduction. The development and adoption of generative artificial intelligence (GenAI) technologies is rapidly expanding, but its integration into psychological services depends on clinician acceptance. Little empirical work has examined how identity-related threat shapes psychology professionals’ adoption intentions of GenAI. The present research tested an extended technology acceptance model (TAM), with the addition of personal and professional identity threats, to explain psychology professionals’ adoption intentions of GenAI technologies for clinically relevant usage. Methods. A total of 201 UK psychology professionals completed a cross-sectional online survey. Three sequential structural equation models were tested, incorporating perceived usefulness, perceived ease of use, and social influence as core predictors, with threats to professional capabilities and self-threat added as extensions. Two open-ended qualitative questions were analysed using content analysis to provide broader contextual insight into current use and barriers to adoption. Results. All models demonstrated acceptable fit (CFI = 0.936–0.938; RMSEA = 0.071–0.072). Perceived usefulness emerged as the strongest correlate of adoption intention across all models (β = 0.67–0.69). Threats to professional capabilities and self-threat were each reliably negatively associated with adoption intention when entered separately, above and beyond core TAM predictors (β = −0.20 and β = −0.16 respectively). Social influence exerted a reliable indirect effect on behavioural intention through perceived usefulness across all models, as did perceived ease of use in the two models incorporating identity threat. Content analysis identified ethical concerns, lack of trust, and professional identity threats as prominent barriers to adoption. Discussion. Findings highlight the importance of considering both classic TAM factors and identity-related concerns when developing GenAI and implementing such technology into psychological services. Full article

We study estimation of globally sparse relation probability matrices from capped binary relation sets under personalized user-level local differential privacy and Byzantine response replacement. A cardinality-calibrated joint personalized relation sketch (JPRS) releases one bit per user. The decoder combines public channel-amplitude weighting with exact sparse feasible projection. We distinguish trimmed honest information, adversarial leverage, and a response-contamination indistinguishability radius. An explicit capped binary block construction yields a sampling lower bound with an explicit low-information truncation. A separate contamination construction gives a finite-sample error floor. For precommitted corrupted identities, the global decoder admits a uniform bound against adaptive valid-output attacks. In the strong-privacy, sufficiently informative regime, sampling and contamination bounds match up to logarithms on bounded-ratio budget classes, subject to the stated sample regularity and corruption-count conditions. The theory uses global sparsity rather than low-rank structure. Across six synthetic families, the global decoder has lower mean loss than the three tested private global baselines, but improves on zero only in the Zipf setting. Three public-data benchmarks further show that low aggregate error can be driven by a merged tail category. These results delimit practical utility; finite-budget attack searches do not certify worst-case performance. Full article

The increasing demand for avocado oil raises the question of whether production and consumption remain within environmentally sustainable limits. This study evaluated a cradle-to-grave avocado oil supply chain, produced in Colombia and consumed in Germany, by integrating Life Cycle Assessment with planetary-boundary-derived environmental budgets. The production and consumption of 1 kg of avocado oil generated 7.13 kg CO₂-eq, 0.0074 kg P-eq, 0.0068 kg N-eq, 0.1875 m²·a crop-eq, and 1.95 m³ world-eq deprived for climate change, freshwater eutrophication, marine eutrophication, land use, and water use, respectively. Monte Carlo analysis (1000 iterations) indicated that the LCIA results were relatively stable with respect to the inventory uncertainty represented in the background data. We compared the environmental impacts associated with a hypothetical annual intake scenario in which the EAT–Lancet recommended intake of unsaturated oils is supplied entirely by avocado oil with allocated Safe Operating Space budgets. Under equal per-capita sharing, the per-person environmental entitlement is identical across geographical perspectives; geographical differentiation arises only when region-specific sharing factors, such as the economic/FCE factor, are applied. The results show that sustainability conclusions depend strongly on the principle used to share the available environmental capacity among products and activities. Full article

The transition from school to work represents a key developmental challenge during adolescence. Career resources—personal, social, and identity-related factors that support career orientation and preparation—are crucial for navigating this transition. From a resilience perspective, the question arises under which risk and protective conditions such resources develop, are maintained, or are jeopardized. This study examines the relationship between psychosocial difficulties, a career-orientation-focused Positive Peer Culture (PPC) intervention, and the development of career resources. The study used a quasi-experimental pre-post design with an intervention and a control group. The sample consisted of 234 lower secondary school students from regular and special education schools who participated at both measurement points. Career resources were assessed with the Career Resources Questionnaire, and psychosocial difficulties with the Strengths and Difficulties Questionnaire. Linear regression analyses were conducted while controlling for baseline values and relevant sociodemographic variables. Exploratory subscale analyses and a process evaluation of the PPC group meetings were also conducted. The results showed no significant overall effect of the PPC intervention on career resources. However, psychosocial difficulties at baseline were a significant negative predictor of career resources in ninth grade. At the subscale level, an association with “Importance of Work in Life,” reflecting a motivational and value-related aspect of career development, emerged at the unadjusted level; however, this association did not remain statistically significant after correction for multiple testing and should therefore be considered exploratory. The process evaluation indicated predominantly positive group experiences, particularly collaborative problem-solving and mutual support, but also challenges regarding participation and implementation within the school schedule. The findings highlight the relevance of psychosocial difficulties for career development and suggest that the role of peer-based social support in career preparation warrants further investigation. Full article

Climate change is increasingly recognized as a significant stressor affecting well-being, yet responses to climate-related distress are heterogeneous and may reflect the interplay of eco-anxiety and trauma-related symptoms. This study used latent profile analysis to identify patterns of climate-related distress and examined their associations with environmental identity, developmental adversity, attachment, prior psychopathology, demographic characteristics, and direct exposure to climate-related events. A sample of 917 Portuguese adults completed measures of eco-anxiety, post-traumatic stress disorder (PTSD) symptom clusters, Disturbances in Self-Organization (DSO) symptoms, environmental identity, childhood adversity, and attachment. Correlates were examined using multinomial logistic regression and MANOVA. A five-profile solution was identified, ranging from minimal symptom endorsement to severe, co-occurring elevations in eco-anxiety, PTSD, and DSO symptoms. Profiles differed in Protection of Nature, but not Connectedness with Nature. Higher-distress profiles were associated with greater direct exposure to climate-related events, childhood adversity, insecure attachment, prior psychopathology, younger age, and female sex, whereas lower-distress profiles were more strongly associated with secure attachment and the absence of previous mental health difficulties. These findings support a person-centered conceptualization of climate-related distress and highlight heterogeneity in the co-occurrence of eco-anxiety and trauma-related symptoms. Climate-related distress may be embedded within developmental, psychological, demographic, and socioecological contexts. Full article

Drone-based red–green–blue (RGB)–thermal tiny person detection must account for the different appearance statistics of visible and thermal sensing. We propose Asymmetric Modality Domain Randomization (AMDR), a training-only strategy that applies modality-conditioned perturbations while preserving image pairing, geometry, annotations, and the deployed detector graph. Using a fixed Quality-aware RGBT Fusion Detector (QFDet*) baseline, we compare AMDR with Operator-Matched Symmetric Domain Randomization (OM-SDR) across eight training seeds. With a fixed epoch-12 checkpoint, AMDR achieves a mean mAP₅₀ of 46.89% with a standard deviation of 0.53 percentage points on RGBTDronePerson, exceeding Base and OM-SDR by 2.40 and 0.96 percentage points, respectively. Across the main 12-condition controlled synthetic-shift suite, both randomized strategies exceed Base in condition-wise mean mAP₅₀, while their overall averages are nearly identical. A separate unseen synthetic thermal column-bias evaluation further probes a spatially structured corruption family absent from training. The weaker VTUAV-det result indicates dataset-dependent behavior rather than universal improvement. AMDR introduces no additional inference-time parameters, floating-point operations (FLOPs), or network branches. Full article

Mahé (Mayyazhi), a French enclave on India’s Malabar coast, remained under French administration from 1721 to 1954, and its French-Indian families have carried the legal and cultural consequences of that rule across generations ever since. This study examines how colonial-era intimacy was configured within the broader structures of colonial governance in a small, decentralised enclave largely absent from mainstream accounts of empire. Combining a structured literary analysis of M. Mukundan’s novel On the Banks of the Mayyazhi with ethnographic fieldwork among Mahé’s French-Indian families, the study applies a four-phase Historiographic Narrative Analysis Framework (HNAF) to trace how face-to-face contact, bilingual schooling, and household ritual mediated colonial authority and shaped patterns of loyalty, mimicry, and inherited identity across generations. Fiction and lived testimony reveal overlapping but non-identical traces of an affectively saturated, person-to-person contact zone whose effects outlasted formal decolonisation by decades and continue to shape Franco-Indian family memory and commemorative practice today. The findings suggest that genealogical continuity, rather than political rupture alone, offers a productive lens for understanding how colonial subjecthood is inherited, reworked, and lived within families long after an empire’s formal end. These findings carry implications for the study of other small, materially thin colonial enclaves and for broader methodological debates about using literary sources genealogically. The study also speaks to SDG 10, particularly its emphasis on social, economic, and political inclusion and on reducing inequalities rooted in origin and status, by showing how colonial hierarchies can persist through citizenship, family memory, and inherited forms of belonging. Full article