Search Results (1,641)

This study investigates the pedagogical landscape of state language instruction in Hungarian-medium schools in Slovakia. Situated within the wider context of European minority language policies, this study explores the institutional ecosystems, didactic approaches and teaching strategies, and the relationship between teacher- and student-centered methodologies in state language instruction. A questionnaire survey based on a self-developed Multi-Level Diagnostic Model was administered to a representative sample of teachers, accounting for 23% of the total Slovak teacher population working in this distinctive sociolinguistic setting (N = 112). Although the results indicate that the educational process is shaped by various factors and there is an endeavor to promote communicative practice, the competence–use gap persists due to the reliance on conventional teacher-centered teaching approaches. This trend is driven by a methodological vacuum, the absence of specialized L2 teaching materials and the lack of modern digital resources; it also suggests that teachers are forced to prioritize instructional security rather than being resistant to innovation. The findings suggest that the current educational system is ready for change, but it requires systemic investment in resources to promote the balanced development of intercultural communicative competence. Addressing the linguistic distance between Hungarian L1 and Slovak L2 through specialized materials may promote a model of additive bilingualism that ensures professional credibility and the protection of minority cultural identity. Full article

Multi-criteria group decision making (MCGDM) under linguistic uncertainty remains a fundamental challenge in applied mathematics, where decision makers seldom assign crisp numerical evaluations and frequently exhibit heterogeneous risk attitudes shaped by behavioural factors. An integrated mathematical framework, hereafter PLR-3WBC (Probabilistic Linguistic Regret-driven Three-Way Bayesian Consensus), is developed to systematically integrate four methodological components that have each been individually validated in the MCGDM literature: representation of decision information with explicit probability mass on linguistic terms; quantification of decision-maker regret and rejoice psychology under linguistic uncertainty; classification of alternatives into three actionable decision regions rather than a single-valued ranking; and group consensus reaching with credal weight aggregation. Each component has demonstrated its effectiveness in its respective domain; the present framework capitalises on their complementary strengths by embedding them within a single pipeline equipped with formal guarantees, an integration that has not been previously reported. The framework integrates five methodological components: probabilistic linguistic term sets (PLTS) for information representation; the Bayesian best–worst method (BBWM) for credal criterion weighting; a regret–rejoice value function adapted to the linguistic domain for behavioural evaluation; three-way decision (3WD) thresholds derived from a loss-function model for actionable classification; and a distance-based consensus reaching process with feedback mechanism for group convergence. A case study on age-friendliness evaluation of twelve aging urban residential communities under an indicator system of five dimensions and eighteen criteria, with four expert decision makers, demonstrates that PLR-3WBC delivers an actionable three-way classification, recovers a transparent group consensus, and produces rankings broadly consistent with classical TOPSIS, VIKOR, PROMETHEE-II, and BWM-TOPSIS (Spearman rank correlation exceeding 0.97), thereby confirming that the integrated framework preserves the ordinal reliability of these established methods, while additionally delivering three outputs that arise from the methodological integration: an actionable three-way classification enabling discrete budget-aligned decisions, credal weight intervals quantifying the depth of expert agreement on criterion importance, and a behavioural reordering of borderline non-dominated alternatives that reflects the loss-averse psychology of the decision panel and would remain hidden under single-method deployment. Sensitivity analyses with respect to the regret aversion coefficient, the loss function parameters, and the consensus threshold confirm that the qualitative classification is stable across a wide parameter envelope, supporting the practical deployment of PLR-3WBC in age-friendly community renewal programmes. Full article

The emergence of multimodal large language models (MLLMs) is opening a new avenue for explainable and interactive intelligent diagnosis in agriculture. However, generic MLLMs still face two major obstacles in plant disease recognition—insufficient fine-grained visual perception and misalignment between visual and linguistic features—which jointly limit diagnostic accuracy. To address these issues, we propose a Qwen2.5-VL-based full-chain fine-tuning framework termed dual-side synergistic low-rank adaptation. Unlike the mainstream paradigm that freezes the vision encoder, our method injects trainable LoRA adapters into both the vision encoder and the large language model, while establishing end-to-end gradient backpropagation across the entire multimodal pipeline. By using the supervision signal from autoregressive text generation (text-supervised visual learning), the framework directly drives deep optimization of visual representations, thereby enabling coordinated alignment between pixel-level perception and semantic-level understanding. We trained Qwen over CDDM and conducted in-domain (CDDM) and cross-domain (PlantVillage) experiments. The results show that the proposed 7B-parameter model achieves 98.8 and 96.0% diagnostic accuracy under in-domain and cross-domain scenarios, respectively. The recognition accuracy of Qwen in the case of cross-domain only decreases slightly, which demonstrates that the MLLM trained by our method exhibits excellent cross-domain recognition capability. This indicates that our method can significantly improve the robustness and generalization ability of MLLM in complex agricultural scenarios. Full article

Digital Transformation (DT) increasingly relies on project-based organizing to develop and deploy new capabilities, yet corporate innovation projects frequently stall not for lack of ideas but because of recurring governance and resource-commitment bottlenecks. This study presents a micro-longitudinal, AI-enabled, and human-reviewed analysis of 711 episodes drawn from 28 weekly project governance meetings across two corporate startup initiatives participating in the same internal incubation program, conducted between November 2024 and April 2025. Employing a six-stage analytical pipeline that combines episode-level segmentation, linguistic tension markers, and a large language model (LLM) classifier, we identify 28 decision-relevant governance tensions, which are then abductively grouped into 13 project governance dilemmas and mapped onto Teece’s dynamic capabilities framework (sensing, seizing, reconfiguring). The key finding is that 62% of dilemmas are structural in nature—reflecting persistent governance design tensions between autonomy and control, compliance and agility, and centralization and decentralization—and that 69% concentrate at the seizing stage, corresponding to resource-commitment and execution decisions. This pattern indicates a governance choke point in corporate DT projects that is structural and decisional rather than ideational. By shifting attention from lagging indicators (overruns) to governance tension leading indicators, the approach supports earlier interventions to reduce decision latency and protect project delivery performance. We further synthesize two incubation-specific meso-level governance dilemmas—stakeholder engagement and compliance vs. agility—that serve as transmission mechanisms between macro structural constraints and micro-level decision bottlenecks. The AI-enabled pipeline is proposed as a replicable early-warning system for project governance tensions in organizations pursuing digital transformation. Full article

The rapid integration of artificial intelligence (AI) into mental healthcare presents opportunities and ethical challenges, particularly for complex conditions like obsessive–compulsive disorder (OCD). In this perspective, we argue for a Dual Imperative: establishing safety architectures for AI-powered therapeutic tools to prevent algorithmic sycophancy (symptom accommodation), while mandating explainable AI (XAI) in prognostic models to ensure clinical auditability. In therapeutics, we propose a Guardian Angel architecture that utilizes patient-specific fear hierarchies and linguistic stance detection to distinguish compulsive reassurance-seeking from legitimate patient questions. This approach transforms potential therapeutic ruptures into opportunities for distress tolerance via the Digital Ulysses Pact, a patient-authorized, algorithmically enforced response prevention protocol. In diagnostics, we address the black box problem in precision psychiatry. We argue that as AI evolves from detection to high-stakes treatment selection, safety and accountability become a prerequisite for clinical application. Although distinct in implementation, these architectures form an integrated framework for aligning therapeutic and diagnostic AI. These architectures are not parallel tracks but a unified ecosystem: A patient’s XAI-audited profile can inform the Guardian Angel’s configuration, while the longitudinal data gathered during therapy enriches diagnostic precision. Grounded in ethical principles and best practices in OCD, this suggests a path toward AI that is auditable in its diagnostic logic, firm in its therapeutic boundaries, and enforceable through emerging regulatory frameworks. Full article

The advancing fluency of large language models (LLMs) raises important questions about their ability to emulate complex human traits, including emotional expression and personality, across diverse linguistic and cultural contexts. This study investigates whether state-of-the-art LLMs can convincingly mimic emotional nuance in English and personality markers in Arabic, a critical under-resourced language with unique linguistic and cultural characteristics. We conduct two tasks across six models: Jais, Mistral, LLaMA, GPT-4o, Gemini, and DeepSeek. First, we evaluate whether machine classifiers can reliably distinguish between human-authored and AI-generated texts. Second, we assess the extent to which LLM-generated texts exhibit emotional or personality traits comparable to those of humans. Our results demonstrate that AI-generated texts are distinguishable from human-authored ones (F1 > 0.95), though classification performance deteriorates on paraphrased samples, indicating reliance on superficial stylistic cues. Emotion and personality classification experiments reveal significant generalization gaps: classifiers trained on human data perform poorly on AI-generated texts and vice versa, suggesting LLMs encode affective signals differently from humans. Importantly, augmenting training with AI-generated data enhances performance in the Arabic personality classification task, highlighting the potential of synthetic data to address challenges in under-resourced languages. Model-specific analyses show that GPT-4o and Gemini exhibit superior affective coherence, while LLaMA performs worse. Linguistic and psycholinguistic analyses reveal measurable divergences in tone, authenticity, and textual complexity between human and AI texts. These findings have significant implications for affective computing, authorship attribution, and responsible AI deployment, particularly within under-resourced language contexts where generative AI detection and alignment pose unique challenges. Full article

Background: Evaluating centralized purchasing performance is a complex multi-criteria decision-making problem involving uncertainty, linguistic assessments, and subjective judgments from internal clients. Existing approaches provide limited support for handling these characteristics simultaneously. Methods: This study proposes a Mamdani fuzzy inference model integrating four criteria: Service Quality, Responsiveness, Compliance, and Collaboration. The fuzzy rule base was developed using expert knowledge and organizational evaluation practices. The model was applied to a real industrial case study based on an annual evaluation conducted collaboratively by four internal evaluators. Results: The model transformed qualitative assessments into an interpretable performance score while capturing interactions among evaluation criteria and handling uncertainty in the evaluation process. Conclusions: The proposed approach provides a structured decision-support framework for evaluating centralized purchasing performance. It enables the integration of linguistic assessments and expert knowledge, offering a flexible and coherent evaluation tool for industrial environments. Full article

This paper addresses the contemporary challenge of religious disaffiliation and the “supermarket mentality” of liquid religion by proposing a prophetic paradigm shift in evangelisation and catechesis. Utilising Richard Osmer’s practical theological framework as a structure, the study identifies a historical shift from the lived apostolic kerygma (fides qua) toward an over-reliance on formal conciliar definitions and Magisterial formulae (fides quae). This diachronic analysis suggests that the current “apparent failure” of institutional engagement is rooted in a linguistic and methodological disconnect. Drawing on the visionary models of St Augustine and St Benedict, and grounded in Karl Rahner’s transcendental theology, the paper proposes a normative way forward: an inductive pedagogy of the heart. This model prioritises the art of accompaniment and the return to elementary, foundational concepts that address the experiential core of the human person. Ultimately, the study argues that restoring the balance between the lived tradition and the contents of the faith is a theological requirement for helping contemporary believers to live their faith in daily life. Full article

As the environmental sustainability of blockchain technology becomes a focal point of public and academic debate, understanding how technically engaged communities frame this issue is increasingly important. This study examines 3000 long-form comments from a highly active sustainability-focused Bitcointalk thread to analyze sentiment patterns, recurring arguments, and the linguistic cues associated with community responses to environmental criticism. Using Natural Language Processing (NLP) methods, we apply Valence Aware Dictionary and sEntiment Reasoner (VADER) sentiment analysis to classify the discourse, n-gram extraction to identify dominant thematic expressions, and a Random Forest model combined with SHapley Additive exPlanations (SHAP) to interpret the lexical features most strongly associated with sentiment polarity. The results show a strongly positive and internally consistent discourse structure: 87.63% of comments are classified as positive, while negative and neutral comments are comparatively rare. The dominant themes emphasize energy consumption as a necessary trade-off for network security, while external criticism is frequently reframed or rejected. Explanatory modeling further indicates that negative sentiment is primarily driven by terms associated with climate risk, damage, and reputational concerns when users respond to criticism. Rather than claiming to capture the cryptocurrency ecosystem as a whole, this study presents a localized case study of one Bitcointalk mega-thread and describes it as a highly homogeneous narrative space shaped by recurrent rebuttal and rhetorical reinforcement. The findings offer a focused contribution to understanding how insider communities construct sustainability narratives around blockchain energy use, while also highlighting the need for broader comparative and network-structural research in future work. Full article

This article explores the structural paradox of the byeong-gut (Korean shamanic healing ritual): why it adheres to the rigid and canonical format of the jaesu-gut (shamanic blessing ritual) instead of adopting a specialized clinical procedure. Critiquing the instrumental trap of previous scholarship that reduces shamanic healing to psychological comfort or social liberation, this study proposes a relational displacement model by integrating Roy Rappaport’s theory of ritual invariance with the relational ontologies of Bruno Latour and Tim Ingold. The article demonstrates that shamanic healing operates through a dual mechanism. First, at the non-discursive (material) level, the ritual functions as an ontological technology that objectifies and displaces individual suffering onto external surrogates. Second, at the discursive (linguistic) level, a meticulous analysis of the manse-baji (invocation chant) illustrates how the patient’s fragmented life is re-assembled into a meshwork of human and non-human agencies. Ultimately, this article argues that the byeong-gut transcends mere functional curing; it serves as a sophisticated knowledge system that re-maps the isolated ego onto a relational cosmology, transforming the Geertzian bafflement of suffering into an intelligible event within a shared and sacred cosmic order. Full article

Recent advancements in Vision-Language Models (VLMs) have enabled robotic systems to leverage model-based understanding and reasoning over visual and linguistic inputs, offering a promising approach for interpreting user intent in human–robot interaction (HRI). In particular, deictic expressions commonly used in object handovers, such as “take this” and “give me that”, cannot be fully interpreted through language alone and require a comprehensive understanding of the speaker’s perspective and the environment. This study proposes a prompt-driven vision-language framework for deictic interpretation in human–robot handover. The system integrates a pre-trained VLM with a hierarchical prompt that decomposes reasoning into intent classification, spatio-temporal grounding, and output self-validation, enabling accurate identification of target objects and goal locations without model fine-tuning. Experimental results demonstrate 100% command interpretation accuracy across multiple interaction scenarios, including pick-and-place tasks, robot-to-human and human-to-robot handovers, and temporal deictic commands. Notably, the system operates under a prompt–command language mismatch, accurately interpreting Korean commands while being guided by English-based prompts. Analysis across progressive system configurations further demonstrates that structured prompting plays a critical role in reasoning performance. These results highlight the effectiveness of a prompt-driven approach for deictic interpretation and spatio-temporal grounding, providing a practical training-free framework for HRI. Full article

Play is a universal activity. Yet there are cultural and linguistic differences in how families engage in adult–child play. In the present study, Thai–English bilingual mother–child dyads completed a toy play task in both languages. The results revealed cross-linguistic differences in bilingual mothers’ and children’s conversation styles. When speaking Thai, the nature of bilinguals’ dyadic play was more adult-centered, characterized by the use of directives by the mothers and use of repetitions by the children, which was congruent with parent–child interpersonal dynamics in high-power-distance Asian cultures. When speaking English, the play session was more child-centered, evidenced by children’s use of directives and encouragements, which was congruent with behavioral norms in low-power-distance Western cultures. Bilingual mothers and children exhibited positive associations in their narrative styles during both the Thai and English sessions. Additionally, the preliminary results provided evidence that cross-linguistic differences in mother–child speech patterns may be moderated by child gender. These findings suggest that the communicative and interactional patterns that bilingual caregivers modeled for bilingual children varied across languages and that preschoolers aligned their behaviors with those exemplified by their mothers. We conclude that bilingualism influences early social communication, with theoretical and applied implications for researchers, educators, and clinicians. Full article

Session dialogue assessment based on machine learning is gradually becoming an effective solution for therapeutic alliance measurement which is an important factor for successful psychotherapy. However, most existing models assume clean and pre-structured dialogue transcripts, whereas real-world counseling documentation often contains heterogeneous case reports. This gap limits the applicability of current automated assessment models in realistic documentation scenarios. In this work, we propose a framework for automated working alliance assessment from complex, multilingual reports. First, language-specific BERT models are fine-tuned to process case reports across different languages, enabling accurate speaker role delineation and dialogue structuring. Second, Gemini-2.5-Flash is leveraged to annotate the dialogues with working alliance ratings. Third, a hybrid feature representation strategy is then developed to jointly capture linguistic style and semantic content from the counseling dialogues. Furthermore, an entropy-based mutual information analysis is conducted to identify the most informative linguistic features. Finally, the extracted hybrid features serve as inputs to XGBoost for alliance assessment. In experiments, the proposed framework shows better performance in the comparison with SOTA methods and generalization ability. Full article

Orthographic depth varies across alphabetic writing systems and plays a central role in spelling acquisition. In immersion education, a second language (L2) is used as a language of instruction for part of the curriculum, such that learners are primarily confronted with its writing system during the initial stages of literacy development. This early exposure may shape the spelling strategies subsequently deployed in the first language (L1), which also corresponds to the dominant language of the surrounding community. This article provides a structured review of key mechanisms involved in spelling acquisition, orthographic depth, and cross-linguistic influence in bilingual and immersion contexts. On this basis, it proposes a conceptual and predictive framework specifying how the orthographic depth of the instructional language modulates spelling strategies and spelling error profiles in L1. Focusing on French-speaking pupils enrolled in immersion programmes with L2s characterised by either predominantly phonemic or opaque orthographies, the framework integrates strategy-based models of orthographic development. The model distinguishes phonological, lexical, and morphographic components of orthographic knowledge and predicts that immersion in phonemic-dominant orthographies favours phonographic dominance and regularisation patterns, whereas immersion in opaque orthographies promotes greater reliance on lexical–orthographic strategies, resulting in distinct and systematic spelling error profiles in French. Full article

Detecting trafficking-related indicators in online advertisements remains a challenging natural language processing task due to ambiguous language, repetitive templates, and evolving euphemistic expressions. This study presents a lightweight transformer-based framework for identifying potential trafficking-related risk indicators in publicly accessible online advertisements using contextual semantic classification and leakage-aware evaluation. The framework combines standardized text preprocessing, duplicate filtering, semantic group-aware dataset partitioning, and DistilBERT-based classification to improve detection reliability while reducing semantic leakage between dataset subsets. The dataset consists of 3000 curated online advertisements collected from escort and service-related platforms, labeled using trafficking-related linguistic indicators derived from prior research, public trafficking typologies, and domain-informed annotation guidelines. On the held-out test set, the framework achieves an accuracy of 88.7% and a macro F1-score of 0.8338 under leakage-aware evaluation conditions, with PR-AUC and ROC-AUC of 0.984 and 0.993, respectively. Same-dataset baseline experiments using TF-IDF logistic regression and TF-IDF SVM classifiers show that while these feature-based models attain higher macro F1-scores on the curated dataset, the proposed framework achieves higher overall accuracy and substantially stronger threshold-independent ranking (ROC-AUC), indicating more reliable probabilistic discrimination across decision thresholds in a recall-sensitive setting. The reported PR-AUC and ROC-AUC values are interpreted as upper-bound estimates within the present evaluation setting, as residual dataset-specific regularities may persist despite leakage-aware partitioning. The framework is computationally efficient, suitable for deployment in resource-constrained environments, and designed as a human-in-the-loop decision-support system rather than an autonomous enforcement tool. Overall, lightweight transformer architectures provide a scalable and operationally realistic approach for identifying trafficking-related risk indicators in online advertisements under leakage-aware evaluation conditions. Full article