Search Results (60)

Mental health discourse may reflect social relationships, cultural norms, and religious factors that shape how individuals express and interpret distress. Existing NLP research on mental health has advanced the detection of depression, anxiety, suicide risk, and related clinical signals using text mining, neural classification, transformer-based models, and, more recently, large language models. However, most systems treat text primarily as a clinical signal rather than examining the social and cultural contexts in which distress is expressed. Arabic NLP research remains even more limited, largely focusing on detecting clinical conditions while overlooking contextual factors that shape mental health questions. This work introduces ContextMental, a multi-label annotation schema and benchmark dataset for modeling sociocultural context in Arabic mental health questions. The dataset contains 2677 questions, including 552 instances with contextual labels, enabling fine-grained analysis of social, cultural, and religious dimensions. An AraBERT-based classification framework is further developed using imbalance-aware optimization, semi-supervised pseudo-labeling, and adaptive threshold calibration. Experimental results indicate that pseudo-label augmentation improves overall classification performance, suggesting that semi-supervised learning can support context-aware Arabic mental health classification. This study provides a context-aware annotation framework, a benchmark dataset, and an AraBERT-based baseline modeling pipeline for Arabic mental health NLP, thereby supporting future research on socially, culturally, and religiously grounded language technologies. Full article

Prime numbers are central to mathematics, yet popular discourse often treats particular primes as if they carried intrinsic messages, personalities, or moral charge. This study asks how that shift from legitimate curiosity to superstition-adjacent pattern making occurs and why it feels persuasive. Using qualitative content analysis of three widely circulated media examples, this paper maps how culturally specific number meanings are produced and transmitted, and how predictable cognitive biases support their plausibility. The analysis pairs anthropological mechanisms of symbolic association, prestige borrowing, community boundary marking, and meme-based diffusion with psychological mechanisms that include Type I error, apophenia, confirmation bias, availability, narrative fallacy, selection effects, survivorship, cultural priming, and authority or celebrity cueing. Across the cases, the results show a recurrent coupling: cultural schemas supply ready-made interpretive templates, while cognitive biases turn salience and coincidence into perceived significance, concentrating attention on narratively convenient primes and obscuring the many alternative patterns that could have been selected. This paper concludes that meanings such as 666 as evil are culture dependent rather than mathematical properties, and that improving public communication about primes requires making selection processes and interpretive frames explicit while preserving legitimate mathematical wonder. Full article

Ethno Sense is defined as a culturally mediated cognitive–perceptual capacity through which individuals discern, select, and interpret mathematically salient structures in socially situated practices. The increasing recognition of mathematics as a culturally situated practice has prompted growing interest in integrating cultural contexts into mathematics education. Approaches such as ethnomathematics and Realistic Mathematics Education emphasize the importance of culture and meaningful contexts; however, a critical gap remains in explaining how individuals perceive and recognize mathematical structures within culturally embedded experiences. This entry addresses this gap by introducing Ethno Sense as a novel conceptual construct. Conceptualized as a pre-formal layer of mathematical cognition, it explains how culturally conditioned perception, interpretive schemas, and value systems shape the recognition of mathematical meaning prior to formalization. It proposes a mechanism comprising contextual indexing, schema activation and selection, and value-informed interpretation. These processes operate dynamically to guide engagement with culturally meaningful phenomena and the identification of mathematical relevance. The entry further positions Ethno Sense as an epistemological foundation for Ethno-Realistic Mathematics Education, supporting authentic context selection and progressive mathematization. By foregrounding culturally mediated perception, it shifts attention from problem solving to recognizing situations as mathematically meaningful. This study contributes a unifying theoretical construct linking cultural experience and mathematical cognition, and outlines implications for practice and future research on culturally situated learning. Ultimately it offers a lens for understanding reciprocal relationships between culture and mathematics across educational contexts. Full article

This paper presents a lightweight semantic retrieval framework driven by an in-memory knowledge graph (IMKG) to overcome the limitations of traditional keyword matching and the prohibitive hardware costs of deep learning models in digitizing ancient Chinese literature. By extracting structured metadata from canonical texts, we construct a dense, bidirectional graph schema. Diverging from resource-intensive neural architectures, our system abandons heavyweight vector embeddings in favor of a highly optimized, template-based heuristic matching engine natively implemented in Java. This purely symbolic approach ensures deterministic execution, zero-dependency deployment, and seamless operation on standard CPU-only servers. To handle complex historical inquiries, the framework integrates a context-aware dialogue manager for multi-turn anaphora and ellipsis resolution, alongside a synergistic tiered caching mechanism. Extensive evaluations on a benchmark of 13,652 annotated queries demonstrate that the system achieves an exceptional intent recognition accuracy of 97.14%, robust context retention, and ultra-low response latency (≤17 ms). Ultimately, this architecture provides a sustainable, highly reproducible, and cost-effective paradigm for the semantic exploration of classical textual heritage, exceptionally suited for small-to-medium cultural institutions. Full article

The classical Chinese language is characterized by a high density of meaning, wide use of polysemy, and strong dependence on history and culture, which pose challenges to existing large language models (LLMs). Retrieval-augmented generation (RAG) technology has become a prevailing option that could address these issues without retraining the model, but most of the existing RAG systems regard structured tables as unstructured text, encoding a whole table into one vector. Such a schema usually hides the row-level semantic information and raises the reasoning cost for LLMs. In this study, we propose a new table-aware row-wise retrieval system in which each row of a table is treated as an individual semantic unit, explicitly (instead of implicitly) reasoning at generation time. We organize the table into row-level vector representations, which makes retrieval more deterministic and semantically interpretable, in particular, for pedagogical or philological datasets. Based on LangChain and integrated with Qwen LLMs, our system can be evaluated experimentally for classical Chinese learning tasks, where we find that compared with the traditional RAG systems, this system improves on retrieval performance, semantic consistency, and explainability, with no model training or extra computation time required. Full article

This qualitative study explores the construction of financial literacy in the context of retirement among young women in Israel, in light of available cultural schemas. The research challenges economic models that rely solely on assumptions of rationality and self-interest, offering a qualitative expansion of the behavioral foundations of financial literacy. Drawing on in-depth interviews of 46 young women, the findings reveal that their perceptions and choices in the financial domain are profoundly shaped by gendered “schemas of devotion”: devotion to work and devotion to family. These schemas give rise to two orientations: women who are more engaged in the financial sphere, characterized by initiative and reflexivity (resistance to the gendered schemas), and women who are more detached, displaying passivity and emotional distance (adoption of these schemas). The study demonstrates how these cultural models contribute to either the reproduction or transformation of gender inequality in retirement. Its main contribution lies in broadening the discipline of economics and positioning gender as a central analytical category—one that is vital for developing more sensitive policies capable of improving the functioning of the economic system and reducing gender gaps. Full article

As artificial intelligence (AI) systems become increasingly embedded in social contexts, understanding how individuals develop trust in AI relative to humans is critical. This study investigates the relative levels of trust in AI agents (embodied and disembodied) versus human social targets (intimate, intermediate, and distant groups), the psychological mechanisms underlying these trust patterns, and the potential cross-cultural differences between China and U.S. Moderated mediation models were tested to gain insights into how deception experience may affect trust via risk and trust propensity, with perceived honesty norms moderating the mediator-to-outcome pathways. Across both cultures, a consistent trust hierarchy emerged—AI was trusted less than close others but more than distant others. It is likely that, in China, embodied AI was evaluated through interpersonal trust schemas, while in the United States, AI was treated largely as a functional tool regardless of embodiment. Together, these findings clarify both the structure and the processes of AI trust, advancing theoretical debates over whether AI trust mirrors interpersonal trust and offering practical insights for designing culturally adaptive, trustworthy AI systems. Full article

A growing body of literature argues in favor of the influence of organizational culture (OC) on firm performance (FP). Yet this consensus often emanates from studies that over-emphasize the direct culture–performance relationship, with methodologies that are deficient in revealing causal mechanisms and prone to giving ambiguous results. To address these gaps, this study proposes and tests an integrated theoretical framework, synthesizing the Schema Theory, Resource-Based View/Capability theory, and Contingency Theory of Firm Performance. This framework establishes a foundational influence mechanism of OC on performance, moving from cognitive schemas to actualized capabilities and environmental fit. Using data from 249 construction firms in Ghana, we employed a three-stage analytical process; using cluster analysis, we identified five cultural clusters, dominated by Clan and Adhocracy culture types (Organic cultures). Cross-tabulation revealed that large and resource-rich firms (D1K1 and D2K2) were more likely to exhibit balanced cultural profiles. Initial analysis using Kruskal–Wallis H Test showed no significant performance difference between balanced and organic clusters. However, when multiple regression was employed to control for firm classification and adverse industry conditions, the Balanced Culture profile emerged as a statistically significant predictor of superior performance. Consequently, we argue that while an Appropriate Culture, one dominated by organic traits and values, provides survival in a challenged environment, the Balanced Culture profile serves as a critical enabler of superior firm performance, once resource constraints and industry stressors are neutralized. Our findings hold particular importance for international–local joint ventures, where cultural alignment is a critical success factor. Additionally, the proposed framework establishes a robust theoretical foundation for future studies, especially those conceptualizing organizational culture as a foundational, independent variable. Full article

This study investigates Armenian editorial conventions for inscriptions and evaluates their compatibility and the possibility of their further integration with international standards of epigraphic editing for open access and equal use. It focuses on the Divan Hay Vimagrut’yan (Corpus of Armenian Epigraphy), launched in the 1960s, which introduced a systematic apparatus for distinguishing diplomatic transcriptions from interpretative reconstructions. Later Armenian publications often simplified these conventions, replacing specialized signs with typographic substitutes. While these changes improved accessibility, they also reduced palaeographic precision and created inconsistencies across editions. Through comparative analysis with the Leiden Conventions and the EpiDoc TEI framework, the research identifies both areas of alignment and points of divergence. Armenian conventions handle missing letters, restorations, redundancies, and abbreviations in distinctive ways, sometimes reassigning the meaning of symbols across different publications. This variation, if not explicitly documented, complicates digital encoding and risks loss of information. Methodologically, this study develops a digital heritage interoperability model that translates local Armenian editorial practices into machine-actionable standards, enabling their integration into international infrastructures such as EpiDoc and FAIR-based cultural heritage systems. The principal contribution of this work is the proposal of a dual-track encoding strategy. One track applies a granular mapping of Armenian signs to the full set of Leiden and EpiDoc categories, ensuring maximum interoperability. The other track preserves a simplified schema faithful to Armenian usage, reflecting local scholarly traditions. Together, these approaches provide both international comparability and cultural specificity. The conclusion is that Armenian inscriptions can be effectively integrated into global digital infrastructures by means of transparent documentation, crosswalk tables, and encoding policies that follow FAIR principles. This ensures long-term preservation, machine-actionability, and the broader reuse of Armenian epigraphic data in comparative cultural heritage research. Full article

This study curates and structures knowledge concerning Buddhist learning resources for tourism, presenting it through a virtual tour of Wat Phra Pathom Chedi Ratchaworamahawihan in Nakhon Pathom Province. Employing a mixed-methods approach that integrates both qualitative and quantitative methodologies, the research first establishes a structured knowledge base. This involves developing a comprehensive metadata schema for cataloging the temple’s diverse resources, including both sacred sites and artifacts, to enhance their searchability and accessibility. Subsequently, this knowledge is rendered into a virtual tour, which serves as an exemplary model of a Buddhist digital learning resource for tourism. The findings reveal the extensive diversity of resources within the temple. The developed virtual tour platform allows users an immersive exploration of the site via 360-degree panoramic views. This research presents significant implications for relevant agencies, offering a scalable model for the digital dissemination of cultural heritage. It is anticipated that this initiative will expand global access to and appreciation of the temple’s cultural value, thereby fostering international interest in visitation. Such engagement is poised to stimulate the local economy and bolster Thailand’s image as a premier cultural tourism destination. Full article

Gender remains a significant yet often subtle dimension of literacy in early childhood education and care (ECEC). Picturebooks and digital texts may introduce young children to patterned cues about how gender is seen, valued, and enacted, sometimes reinforcing binary expectations even when such messages are not explicit. This paper considers how children may encounter and interpret gender through schemas, scripts, and multimodal features embedded in the texts they read and the literacy practices they participate in. Drawing on insights from picturebook scholarship, cognitive studies, queer theory, and childhood studies, the discussion explores how gender may be shaped through repeated visual, verbal, and affective cues that children learn to recognise and respond to. At the same time, a growing body of inclusive and counter-normative texts may offer opportunities for children to expand or adjust their existing understandings of gender, although such shifts are often partial and dependent on context, mediation, and broader cultural messages. By approaching literacy as an embodied, relational, and multimodal experience, this paper aims to open a reflective space for considering how early literacy practices may support more diverse and expansive possibilities for gender in ECEC settings. Full article

Recent text to image systems can synthesize Islamic heritage elements with high visual fidelity, but their outputs rarely translate into fabricable geometry or integrate into interiors without substantial redrawing. We present an end-to-end workflow that links historically grounded precedent retrieval, controllable tileable generation, semantic segmentation and vectorization, and geometry-aware mapping into Computer-Aided Design (CAD) environments. Contributions include the following: (i) a license-audited dataset schema and a retrieval classifier for common Islamic motif families and architectural elements; (ii) precedent retrieval via a ResNet 50 and Vision Transformer (ViT) embedding pipeline; (iii) a Low-Rank Adaptation (LoRA) tuned diffusion model that generates tileable motifs with motif/region controls; (iv) a raster-to-vector pipeline that enforces curve closure and minimum feature widths for CNC/laser fabrication; and (v) a rubric and domain metrics (symmetry coherence, seam/tileability error, spline closure and junction valence, UV distortion, feature width compliance) that quantify “depth of integration” beyond surface texture. Quantitative metrics and blinded expert ratings compare the workflow against strong parametric baselines, while scripts translate images to fabrication-ready vectors/solids across walls, ceilings, partitions, floors, and furniture. Cultural safeguards cover calligraphy handling, regional balance audits, and provenance/credit. The workflow advances heritage-aware generative design by carrying imagery across the last mile into buildable detail and by providing practical checklists for adoption in interior architecture and conservation. Full article

Gemstone samples, as objects of study in gemology, carry rich geological information and cultural value, playing an irreplaceable role in teaching, research, and public science communication. In the current age of big data, machine learning and artificial intelligence techniques based on gemstone databases have emerged as a cutting-edge area of gemology. However, traditional gemstone databases have three major limitations: an absence of standardized data schemas, incomplete core datasets (e.g., records of synthetic and treated gemstones and inclusion characteristics), and poor data interoperability. These deficiencies hinder the application of advanced technologies, such as machine learning (ML) and AI techniques. This paper reviews gemstone data and applications, as well as existing gem-related sample databases, and proposes a framework for a new gemstone database based on standardization (FAIR principles), integration (blockchain technology), and dynamism (real-time updates). This framework could transform the gemstone industry, shifting it from “experience-driven” to “data-driven” practices. Powered by big data technology, this novel database will revolutionize gemological research, jewelry authentication, market transactions, and educational outreach, fostering innovation in academic research and practical applications. Full article

Japanese narcissism refers to a culturally embedded form of narcissistic personality that emerges within collectivist societies, particularly in Japan, where self-worth is maintained through emotional over-adaptation, perfectionism, self-sacrifice, and conformity to internalized moral obligations. Within the framework of Schema Therapy, this construct is characterized by dominant coping modes, such as Armor mode and Demanding Community mode, that suppress vulnerable emotional states and promote socially sanctioned compliance. Although narcissistic personality disorder (NPD) has been extensively studied in individualistic Western cultures, its manifestation in collectivist cultures remains underexplored. Japanese narcissism offers a culturally contextualized model that integrates psychoanalytic and Schema Therapy perspectives to explain thin-skinned narcissistic vulnerability, disguised as adaptive functioning. Clinical observations and case analyses indicate that patients often develop Armor mode (fusing Detached Protector and Perfectionistic Over-controller functions) and Demanding Community mode (internalizing collective moral expectations). These adaptive-appearing modes mask core maladaptive schemas—Emotional Deprivation, Defectiveness/Shame, Enmeshment, and Self-Sacrifice—while being mistaken for mature or healthy functioning. Historically, such patterns have been reinforced by moral-collectivist ideals, exemplified by the Imperial Rescript on Education, which valorized loyalty, endurance, and self-denial. Japanese narcissism may therefore represent a culturally specific clinical configuration, suggesting the need for contextually adapted Schema Therapy interventions that recognize both the harmony-preserving and narcissism-reinforcing functions of adaptive behavior. This framework contributes to the cross-cultural extension of Schema Therapy by theorizing how narcissistic structures manifest in collectivist societies, and highlights the need for empirical validation of culturally sensitive treatment protocols. Full article

For more than a decade (2014–2025), the Consortium “3D for Digital Humanities” has been advancing the use of 3D technologies in the Humanities and Social Sciences (HSS) while structuring and supporting the research community. It now brings together more than 30 teams, primarily from academic research, but also increasingly from the cultural sector. Under its coordination, significant achievements have been realized, including best-practice guides, an infrastructure for the publication of 3D data, and dedicated software for documentation, dissemination, and archiving, as well as a metadata schema, all fully aligned with FAIR principles. The Consortium has developed national training programs, particularly on metadata and ethical practices, and contributed to important initiatives such as the reconstruction of Notre-Dame de Paris, while actively engaging in European projects. It has also fostered international collaborations to broaden perspectives, share methodologies, and amplify impacts. Looking ahead (2025–2033), the Consortium aims to address the environmental impact of 3D data production and storage by proposing best practices for digital sustainability and efficiency. It is also expanding the National 3D Data Repository, enhancing interoperability, and adopting emerging standards to meet evolving scientific needs. Building on its past achievements, the Consortium intends to further advance 3D research and its applications across disciplines, positioning 3D data as a key component of future scientific data clouds. Full article