Abstract: Asking questions is fundamental, but without a systematic framework, it remains a matter of intuition rather than design. The Generalized Coordinate System (GCS) was initially proposed for analyzing and generating rhetorical modes. In this paper, we apply the GCS to form an inquiry design framework—the GCS-based 10-dimensional inquiry generation framework: treating a question as a coordinate point across ten axes, so that we have potentially a billion ways to ask questions. The five low-dimensional axes (Thing, Feature, Quantitative Attribute, Qualitative Attribute, Formal Attribute) determine what and how the question expresses; the two mediating axes (Basic Element, Rhetorical Mode) transform a raw inquiry into a communicable question package; the three high-dimensional axes (Cognitive Function, Epistemic Purpose, Expression Staircase) determine what mental operation, why, and at what developmental level. This GCS-based 10-dimensional inquiry generation transforms questioning from an intuitive art into a designable, transferable, and evaluable cognitive methodology, and is potentially useful in applications such as education, research, communication, and language modeling.

Abstract: 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.

Abstract: The intensification of ESG disclosure requirements under the Corporate Sustainability Reporting Directive (CSRD) and the International Sustainability Standards Board (ISSB) has increased the demand for artificial intelligence (AI) and data analytics to support large-scale sustainability reporting and verification. However, the existing academic literature remains fragmented across disciplinary domains, including natural language processing, machine learning, auditing, and regulatory compliance. This study addresses this gap through a PRISMA 2020-compliant systematic literature review of 45 peer-reviewed articles published between 2020 and 2025 and indexed in the Scopus database. The analysis combines bibliometric techniques using VOSviewer with qualitative thematic content analysis. The results reveal a rapidly expanding research field with a compound annual growth rate of 91.9%. Four major thematic dimensions emerge: (i) NLP and text mining for ESG disclosure analysis; (ii) machine learning applications for ESG scoring and corporate performance; (iii) AI-enabled ESG assurance, auditing, and governance; and (iv) regulatory frameworks and the digital transformation of sustainability reporting. The findings indicate that AI technologies are progressively transforming ESG disclosure from a predominantly narrative and self-reported practice into a data-driven and verifiable transparency system. These developments have important implications for regulators, corporate practitioners, assurance providers, and investors seeking to enhance the reliability and comparability of sustainability disclosures.

Abstract: This paper presents WuYi (五仪 "Five Rites"), a methodology for learning Chinese characters based on a three-level cascade architecture that integrates sequential multimodal encoding, inter-item narrative chaining, and a culturally grounded macro-narrative organized according to Wu Xing (五行) philosophy and classical Chinese mythology. At the micro level, five cognitive modalities—mental visualization (Fire), phonological construction (Metal), kinesthetic anchoring (Wood), theatrical episodic simulation (Earth), and graphomotor reconstruction (Water)—are activated in a prescribed sequence with explicit transition criteria, overcoming working memory limitations through temporal unfolding rather than parallel presentation. At the meso level, 2–5 radicals are linked through continuous causal narratives that simultaneously serve discriminative, compositional, and retrieval functions. At the macro level, the entire corpus of 214 Kangxi radicals is distributed across a two-cycle mythological structure—Cosmogonic Cycle (74 radicals) and Legendary Cycle (131 radicals)—each traversing five Wu Xing phases aligned with the canonical mythology of Nüwa, Shennong, Huangdi, and Fuxi.The methodology introduces several novel mechanisms: synthetic narrative calligrams that encode tonal contours through typographic modulation; chimeric tone spirits that bind homophonic morphemes across all four tones into single mnemonic characters; deferred mnemonic anchors that create proactive facilitation through spreading activation; and narrative-aligned primary encoding with autonomous fallback mnemonics activated through self-diagnosis. The theoretical framework integrates dual coding theory, levels of processing, embodied and situated cognition, cognitive load theory, the SPT effect, hierarchical retrieval cues, and narrative transportation theory. A between-subjects experimental protocol (N=90, three groups) for controlled validation is provided. No prior work was found that combines sequential multimodal cascade encoding, inter-item narrative chaining, or mythological macro-organization of character curricula.

Abstract: This study advances the Law of the Trio as a universal law of linguistics, positing that reality, thought, and language are ontologically equivalent yet formally distinct modalities of existence. Unlike prior frameworks that isolate language as computation, code, or communicative tool, the Trio establishes a foundational architecture: the recursive coupling of entity and state/behavior, enriched by layered modifiers. Sentences are reframed as semantic DNA, encoding identity, transformation, and relational depth across modalities. To formalize this claim, the paper introduces EMi/VMi,j notation, where i indexes modifier type and j denotes recursion depth. Worked examples and cross‑linguistic analysis (English, Korean, Basque) confirm semantic invariance across typologically distinct languages. Direct mapping to event semantics and thematic roles highlights both alignment and innovation, with recursion depth providing a computable dimension absent from existing models. Comparative analysis shows how the Trio consolidates and extends generative grammar, cognitive science, pedagogy, and semiotics by resolving their limitations through recursive semantic geometry. Applications in pedagogy and natural language processing demonstrate practical relevance. By restructuring linguistics into semantic geometry, the Trio offers a testable, falsifiable, and universal law of language that unifies theory and practice.

Abstract: This study explores the sensitivity differences between behavioral experiments and verbal reports in translation quality evaluation. Results indicate that behavioral metrics (e.g., response times) are significantly more sensitive to syntactic-pragmatic manipulations (phrase order) than verbal reports. Translations with congruent phrase order received higher ratings and faster response times compared to those with incongruent order. However, most participants explicitly denied phrase order's influence in verbal reports. Lexical equivalence showed no significant impact on explicit ratings but increased cognitive effort, as indicated by slower response times for approximate lexical matches. These findings reveal a critical dissociation between implicit cognitive processes and explicit awareness in translation evaluation. The study highlights that translation assessment involves both implicit System 1 processes and explicit System 2 reasoning, offering new cognitive insights for translation research and practical implications for translator education and machine translation assessment. By bridging cognitive science and translation studies, this research contributes to a paradigm shift: translation quality is not merely what evaluators say it is, but what their cognitive behavior reveals it to be.

Abstract: Loanword Research on Anglicisms has largely centered on lexical borrowing and phonological adaptation, with comparatively limited attention to morphosyntactic integration in recipient grammars. This study examines the syntactic behavior of single-word Anglicisms in Mexican Spanish, drawing on phonetically classified corpora of 131 monosyllabic Anglicisms with mon-ophthongs extracted from spontaneous speech by Spanish–English bilinguals in the Tijuana–San Diego border region. Building on prior acoustic analyses based on F1 and F2 vowel measure-ments, the study investigates the relationship between phonological adaptation and morphosyn-tactic integration. Results reveal a gradient pattern of incorporation. Anglicisms exhibiting Span-ish-like phonetic properties tend to occupy canonical syntactic positions and show greater com-patibility with Spanish functional morphology, whereas phonetically non-adapted forms more frequently resist morphological marking and display island-like behavior within otherwise Spanish clauses. The analysis examines distribution across nominal, adjectival, and prepositional domains, as well as object positions, enabling a fine-grained assessment of degrees of morpho-syntactic integration. The former is illustrated as follows: (1) Guardo cash ([kaʃ]) por si acaso (2) Si hacen match ([mæʧ]), puede funcionar Adopting a usage-based and contact-oriented perspective for syntactic borrowing (Bybee, 2015), the study is situated within the Matrix Language Frame model (Myers-Scotton, 1993; Muysken, 2000) and recent approaches to insertional borrowing (Poplack & Dion, 2012; Onysko & Win-ter-Froemel, 2011). A central contribution lies in establishing a principled link between morpho-syntactic behavior and an independently motivated phonetic classification, offering convergent evidence for the systematic integration of Anglicisms into Spanish grammar. At a broader ana-lytical level, the study advances debates on syntactic borrowing and contact-induced change by demonstrating that Anglicisms are subject to Spanish morphosyntactic constraints rather than functioning as unconstrained lexical insertions, and by developing an interface-based account of borrowing that captures the gradient nature of grammatical incorporation in contact settings and contributes a corpus-based, empirically grounded perspective to typologies of borrowing in Spanish contact linguistics.

Abstract: Translation process research (TPR) relies on objective behavioral data to uncover the cognitive mechanisms underlying translation. This paper provides a comprehensive methodology for using Translog-II, a specialized tool for recording user activity data during translation tasks. We outline the complete experimental workflow—from project configuration to data collection—demonstrated through an English-to-Chinese translation-from-scratch case study. The study details the integration of Translog-II with the CRITT Translation Process Research Database (TPR-DB) to facilitate advanced post-processing. Key technical challenges are addressed, specifically the complexities of keystroke-to-word mapping for logo-graphic scripts requiring Input Method Editors (IMEs). We further demonstrate automated alignment protocols, multidimensional error annotation, and data visualization techniques utilizing Python scripts and Shiny R interfaces. The results indicate that while automated mapping is generally robust, specific technical noise, particularly regarding long deletions, can be mitigated through systematic analysis. Ultimately, this protocol establishes a reproducible framework for exploring translator behavior, enhancing the precision of data-driven insights into cognitive translation processes.

Abstract: The integration of artificial intelligence (AI) in digital classrooms has introduced both opportunities and challenges for academic honesty. This narrative review study explored how AI tools influence students’ learning behaviors, assessment practices, and ethical decision-making in academic tasks. Data were collected from students and educators through surveys, interviews, and document analysis, focusing on AI-assisted writing, digital platforms, and institutional policies. Findings reveal that while AI can enhance learning efficiency and engagement, it also blurs the boundary between legitimate academic support and misconduct. Many students perceive AI use as similar to peer assistance, resulting in uncertainty regarding ethical practices. Lower proficiency students were particularly prone to reliance on AI-generated outputs, highlighting the need for targeted instructional support. Traditional assessment formats, such as essays and take-home assignments, were identified as vulnerable to AI misuse, prompting calls for process-oriented evaluations, reflective tasks, and in-class assessments. The study also emphasizes the importance of clear institutional policies and AI literacy programs in promoting responsible use. Moreover, emerging technological risks, including deepfake content, underscore the necessity of proactive guidance and monitoring. Overall, the research suggests that fostering academic integrity in AI-mediated classrooms requires a balanced approach, combining ethical education, innovative pedagogy, and policy development. By cultivating transparency, critical thinking, and responsible AI engagement, institutions can maximize AI’s educational benefits while safeguarding authenticity and integrity in student work.

Abstract: This paper introduces the Generalized Coordinate System (GCS) as a framework for analyzing and generating rhetorical modes---the conventional patterns of discourse. The GCS is composed of ten axes: Thing, Feature, Quantitative Attribute, Qualitative Attribute, Formal Attribute, Basic Element, Rhetorical Mode, Cognitive Function, Epistemic Purpose, and the Five-Level Expression Staircase. The first six axes represent lower-dimensional components, the seventh serves as the ontological axis for rhetorical modes, and the final three constitute higher-dimensional components. Three types of semantic or modal mapping are defined: low-dimensional mapping (from lower-dimensional axes to the ontological axis), high-dimensional mapping (from the ontological axis to higher-dimensional axes), and full-dimensional mapping. These mappings form a pyramidal hierarchy, progressing from foundational elements (things, features, and attributes) to higher-order cognitive functions and epistemic purposes. By employing three core logical structures---combinatory, parallel, and embedded---the GCS consolidates infinite expressive possibilities within the finite intersections of its axes. The system's generative capacity, quantifiable by the number of axis intersections (generalized mode number), enables the navigation of nearly infinite expressive variations while steering practical applications toward finite, purpose-driven goals. The GCS transitions rhetorical modes from a static taxonomy to a dynamic analytical system for discourse construction and analysis, offering possibly insights for the development of large language models through the integration of a programmable rhetorical mode system.

Abstract:

This article identifies a terminological misrepresentation in the expression ‘small gatherings cancellation’—ranked by Haug et al. (2020) as the most effective non-pharmaceutical intervention during the COVID-19 pandemic. Corpus-based and theoretical analyses demonstrate that small gathering conventionally denotes a planned or spontaneous social event, whereas the predicate cancellation reinforces this event-based frame. Consequently, the phrase fails to capture the intended reference to restrictions on simultaneous presence in commercial or professional settings. Drawing on cognitive-linguistic theory and institutional usage from the WHO and CDC, this paper shows how such misrepresentation may trigger unintended conceptual frames, leading to interpretive ambiguity in both scholarly and policy contexts. Three alternatives are proposed to achieve better semantic alignment and enhance terminological precision and communicative clarity in future public-health discourse.

Abstract: Background/Objectives: Sichuan Mandarin is often described as exhibiting overlap or merger between word-initial /n/ and /l/, but perceptual sensitivity across phonetic contexts remains underexplored. This study examines whether perception of the /n–l/ contrast varies by vowel context and listener experience. Methods: Thirty-two Sichuan Mandarin listeners completed categorical identification and same–different AX discrimination tasks using seven-step /n/→/l/ continua derived from native-speaker productions in /i/ and /a/ contexts. Sensitivity, response bias, accuracy, and response times were analyzed alongside individual differences. Acoustic properties of the stimuli were quantified using spectral and amplitude-based measures. Results: Listeners showed overall reduced sensitivity to the /n–l/ contrast, with substantially stronger perceptual differentiation in /i/ than /a/ context. Bias patterns were comparable across contexts, indicating sensitivity-driven effects. Acoustic analyses showed more robust cue structure in the /i/ continuum. Age, education, and Standard Mandarin experience modulated response efficiency but did not eliminate the vowel asymmetry. Conclusions: Results support a context-dependent near-merger of /n/ and /l/, shaped by acoustic cue availability and experience-based cue exploitation.

Abstract: The large digital archives of the American Physical Society (APS) offer an opportunity to quantitatively analyze the structure and evolution of scientific communication. In this paper, we perform a comparative analysis of the language used in eight APS journals (Phys. Rev. A, B, C, D, E, Lett., X, Rev. Mod. Phys.) using methods from statistical linguistics. We study word rank distributions (from monograms to hexagrams), finding that they are consistent with Zipf’s law. We also analyze rank diversity over time, which follows a characteristic sigmoid shape. To quantify the linguistic similarity between journals, we use the rank-biased overlap (RBO) distance, comparing the journals not only to each other, but also to corpora from Google Books and Twitter. This analysis reveals that the most significant differences emerge when focusing on content words rather than the full vocabulary. By identifying the unique and common content words for each specialized journal, we develop an article classifier that predicts a paper’s journal of origin based on its unique word distribution. This classifier uses a proposed “importance factor” to weigh the significance of each word. Finally, we analyze the frequency of mention of prominent physicists and compare it to their cultural recognitions ranked in the Pantheon dataset, finding a low correlation that highlights the context-dependent nature of scientific fame. These results demonstrate that scientific language itself can serve as a quantitative window into the organization and evolution of science.

Abstract: This paper reports a cognitive psychology experiment and a Markov decision process (MDP) model of the production effect—higher memory retrieval that follows speaking aloud or writing/typing words, as opposed to lower memory retrieval when words are read silently. Current models of the production effect draw on the global-matching framework of memory. We identify four limitations of these models and present a MDP model (a perceptual active inference model) to causally explain a superior production effect of speaking over writing. University students performed a word-production task comprising speaking and writing conditions, followed by a memory test. The results showed main effects of condition on accuracy and response times. The MDP model indicated higher sensory precision during memory retrieval in the speaking condition than in the writing condition. Through Bayesian model selection, we evaluated whether the MDP model, as a mechanistic active-inference model, provided higher construct validity than a descriptive linear model (fit via Variational Laplace). The MDP model outperformed the linear model, suggesting that production modalities are hidden states that cause the visual sensory observation of words that had been linguistically produced. Crucially, the MDP model explains both group effects and individual variability, confirming the reliability paradox of statistical models.

Abstract: This paper proposes Trace & Trajectory (T&T) Semantics, a pre-representational framework for understanding meaning as intent-driven navigation through informational space. Motivated by fieldwork with multimodal, intersubjective communication—where meaning emerges through gesture, prosody, and embodied coordination rather than propositional structures—I extend Hoffman and Prakash's trace logic to continuous semantic trajectories. The framework models meaning not through Euclidean feature spaces but through attractor dynamics: meaning stabilizes where intent-driven trajectories converge under dissipative constraints, creating basins that guide navigation without representational anchoring. The critical innovation is operator σ's fractal architecture. As meta-awareness intensifies, trace patterns achieve self-similarity across scales, enabling collapse and reconjunction without infinite regress. This mechanism naturalizes prototype effects, conceptual metaphor, image schema stability, and abstract reasoning as emergent from how conscious agents navigate meaning-space under intent, dissipation, and σ-modulation—not from mental representations. T&T dissolves the hard problem of semantic content by grounding meaning in informational dynamics during concrete intersubjective engagement, where patterns maintain semiotic coherence through intent-driven navigation, without reference to external representational targets. This preserves systematicity while respecting embodied intuition. The framework offers cognitive linguists, anthropologists, and semantic theorists an approach that is formally rigorous (utilizing attractor dynamics, Markov kernels, and σ-operators), empirically tractable (applicable to actual discourse and interaction), and phenomenologically adequate. Crucially, the formalism describes patterns in conscious, intentional dynamics—not neural mechanisms—making it appropriate for phenomena in which agent purpose drives semantic organization.

Abstract: Traditional linguistic analysis segments gestures and signs into discrete morphemes—handshape, location, movement—treating these as combinable building blocks. This segmentation, however, reflects analytical resolution rather than ontological structure. At coarse-grained analysis, continuous trajectorial dynamics \textit{appear} segmented because fine distinctions fall below the analytical thresholds of the model's toolkit. This paper argues that gestures and signs function as complete phrasal units whose meaning emerges through navigation rather than morphemic assembly. The supposed ``atoms'' of manual-visual communication are observational artifacts generated by insufficient analytical resolution. We posit that the fundamental unit of gesture and sign is not the morpheme but the trajectory—a continuous navigational arc through informational space guided by conventional traces. Our high-definition approach refers not to perceptual refinement but to analytical granularity: developing theoretical tools capable of tracking trajectorial dynamics at finer informational scales without imposing artificial segmentation. Conventional traces saturate through repeated navigation into specialized attractors (gestural configurations, modal semantics, conceptual prototypes) that emerge as differentiated regions within pre-representational informational space, analogous to stem cell specialization into distinct tissues. This trajectorial approach resolves persistent paradoxes in classifier-predicate analysis, verb segmentation, and gradient iconicity by recognizing them as resolution-dependent phenomena. The analysis extends beyond manual-visual modalities: spoken utterances reveal trajectorial character when examined at sufficient spatiotemporal resolution, challenging written-language models that artificially atomize continuous phonetic-semantic flow. Gestures and signs, precisely because they resist orthographic reification, provide exceptionally clear windows into the trajectorial dynamics underlying all human meaning-making.

Abstract: This study examines the presence and significance of root words derived from non-Arabic languages in the Holy Quran, with a focus on their Hebrew, Aramaic, Syriac, Greek, Persian, and Ethiopic origins. At the same time, the Quran is traditionally regarded as a purely Arabic revelation, but linguistic and historical evidence reveal the integration of foreign lexical elements into its discourse. This research examines how these borrowed roots were phonologically adapted, morphologically assimilated, and semantically recontextualised through a comparative linguistic analysis, aligning with Quranic themes and theological narratives. The findings indicate that such lexical incorporations were not incidental but somewhat reflective of the multilingual and multicultural context of 7th-century Arabia. Furthermore, the study emphasises the Quran's dynamic linguistic environment, which enabled it to engage diverse audiences while maintaining its claim of ʿArabī mubīn (clear Arabic). By examining selected root words and their original meanings, this paper underscores the Quran’s role as a unifying spiritual text and a linguistic artefact shaped by historical intertextuality. This analysis contributes to broader discussions in Quranic linguistics, comparative Semitic philology, and Islamic theological thought.

Abstract: This study presents a comprehensive bibliometric review of robot-assisted language learning (RALL) from 2003 to 2025, analyzing 439 publications from Web of Science, Scopus, PubMed, and Dimensions. Using Biblioshiny and VOSviewer, we mapped publication patterns, citations, keyword networks, and thematic evolution. Findings revealed steady growth peaking at 71 publications in 2023 before a slight decline in 2024, with China, the Netherlands and the United States emerging as the leading contributors and most cited nations. Keyword clustering identified four themes: educational robot, artificial intelligence, human-robot interaction and children. Thematic evolution analysis revealed a shift from foundational research to a multidisciplinary domain integrating AI, VR, IoT, and LLMs, emphasizing learner-centered designs. However, research remains fragmented and technology-driven rather than grounded in pedagogical frameworks. This review calls for bridging the gap between innovation and theory-grounded robot design. Only through interdisciplinary collaboration and evidence-based practice can RALL fulfill its transformative potential in language education.

Abstract: Listeners vary in their perception of speech, falling along a continuum from categorical to continuous. We applied a Bayesian computational framework to model this individual difference in speech perception. We analyzed publicly available data (Honda et al., 2024) from 195 participants across four phonetic conditions using both two-alternative forced choice and visual analogue scale tasks. Our model characterizes each listener’s perception using two key parameters: perceptual warping (τ), the signal-to-noise ratio of phonetic encoding, and noise variance (\( \sigma_S^2 \)), a proxy to perceptual noise in experimental designs. Combining these two parameters revealed four perceptual profiles: Veridical (high τ, low \( \sigma_S^2 \)), Categorical (low τ, low \( \sigma_S^2 \)), Compensatory (low τ, high \( \sigma_S^2 \)), and Noisy (high τ, high \( \sigma_S^2 \)). These profiles predicted behavioral patterns coherently, while successfully distinguishing between listeners who would appear similar when characterized by behavioral measures alone. Critically, results revealed that profile distributions shifted dramatically based on phonetic conditions, with primary cues yielding a balanced mix of profiles and secondary cues producing distributions skewed heavily toward Veridical and Compensatory listeners (80%). Underscoring this flexibility, intraclass correlations for both τ and \( \sigma_S^2 \) ​ were zero, with phonetic condition effects 30 times stronger for \( \sigma_S^2 \) (χ² = 803.91) than τ (χ² = 29.47). These findings challenge the traditional view of categorical perception as a fixed characteristic, demonstrating instead that it is a flexible, context-driven perceptual state.

Abstract:

This study introduces the root mean square error (RMSE) as a new metric for quantifying gradient speech perception in visual analog scale (VAS) tasks. By measuring the deviation of individual responses from an ideal linear mapping between stimulus and percept, RMSE offers a theoretically transparent alternative to traditional metrics like slope, response consistency, and the quadratic coefficient. To validate these metrics, we first used simulated data representing five distinct perceptual response profiles: ideal gradient, categorical, random, midpoint-biased, and conservative. The results revealed that only RMSE correctly tracked the degree of true gradiency, increasing monotonically from the ideal gradient profile (RMSE = 5.48) to random responding (RMSE = 42.16). In contrast, traditional metrics failed critically; for example, slope misclassified non-gradient, midpoint-biased responding as highly gradient (slope = 0.24). When applied to published empirical VAS data, RMSE demonstrated strong convergent validity, correlating robustly with response consistency (r ranging from -0.44 to -0.89) while avoiding the ambiguities of other measures. Crucially, RMSE exhibited moderate-to-high cross-continuum stability (mean r = 0.51), indicating it captures a stable, trait-like perceptual style. By providing a more robust and interpretable measure, RMSE offers a clearer lens for investigating the continuous nature of phonetic categorization and individual differences in speech perception.