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16504 papers
Cognitive reappraisal and attentional distraction constitute two core strategies for regulating emotions. Prior studies have largely focused on young adults regulating simple laboratory stimuli, with few direct comparisons of brain regions that differentiate or mutually implement these strategies. Here, we expanded the typical age range of participants, compared reappraisal and distraction within participants, and used ecologically valid autobiographical memories as regulatory targets. Sixty-two healthy adults aged 35-75 years generated cue words for negative and neutral autobiographical memories and were trained to either reappraise, distract, or let their emotions flow naturally in response to cued memories. Strategy-specific contrasts were derived from whole-brain fMRI data using univariate analyses. For reappraisal, relative to flow, we observed activity in bilateral occipital cortex, right cerebellum, and cingulate cortex and primarily left-sided frontal, temporal, and parietal cortices. Distraction, relative to flow, engaged bilateral lateral prefrontal, medial parietal, cingulate, occipital, and retrosplenial regions and left cerebellum. Common areas of activation included midline occipital and posterior cingulate cortices. Direct comparisons yielded strategy differences across multiple cortical areas: distraction engaged paralimbic areas (insula and left parahippocampal gyrus), dorsolateral and ventrolateral PFC, and right inferior frontoparietal cortex, whereas reappraisal engaged dorsomedial PFC, left ventrolateral PFC, anterior temporal cortex, and left posterolateral PFC. In-scanner valence ratings verified the efficacy of the experimental manipulation and revealed a negative impact of age on reappraisal success, which was correlated with greater visual cortical processing. These findings extend knowledge regarding the neural mechanisms of emotion regulation across the adult lifespan for autobiographical events.
Grapheme-to-phoneme (G2P) conversion for Persian presents unique challenges due to its complex phonological features, particularly homographs and Ezafe, which exist in formal and informal language contexts. This paper introduces an intermediate language specifically designed for Persian language processing that addresses these challenges through a multi-faceted approach. Our methodology combines two key components: Large Language Model (LLM) prompting techniques and a specialized sequence-to-sequence machine transliteration architecture. We developed and implemented a systematic approach for constructing a comprehensive lexical database for homographs with multiple pronunciations disambiguation often termed polyphones, utilizing formal concept analysis for semantic differentiation. We train our model using two distinct datasets: the LLM-generated dataset for formal and informal Persian and the B-Plus podcasts for informal language variants. The experimental results demonstrate superior performance compared to existing state-of-the-art approaches, particularly in handling the complexities of Persian phoneme conversion. Our model significantly improves Phoneme Error Rate (PER) metrics, establishing a new benchmark for Persian G2P conversion accuracy. This work contributes to the growing research in low-resource language processing and provides a robust solution for Persian text-to-speech systems and demonstrating its applicability beyond Persian. Specifically, the approach can extend to languages with rich homographic phenomena such as Chinese and Arabic
Исследование посвящено совершенствованию метрик релевантности эмоционального контекста в системах извлечения контекста для Retrieval-Augmented Generation. Современные подходы фокусируются на семантическом сходстве текстов, пропуская эмоциональную составляющую, когерентность, что критично для задач наиболее точного поиска в эмоционально окрашенных данных. Предложена композитная метрика, объединяющая семантическое сходство векторных эмбеддингов и эмоциональную когерентность, оцениваемую как косинусное сходство текстов. Описана методология вычисления компонент метрики и их взвешивания, на основе которой проведен ряд экспериментов с целью выяснения способности предложенной метрики находить текст более точно, чем модель, основывающаяся на поиске только семантической составляющей. Оценка эффективности проведена на сэмплированном подмножестве датасета Stanford Sentiment Treebank 2 (SST-2). Композитная метрика продемонстрировала более высокую эффективность по метрикам Precision@5, Recall@5, NDCG@5 и MAP по сравнению с методами поиска, основанными исключительно на семантической составляющей. Проведенные эксперименты показали значительный прирост метрик для позитивных и негативных запросов. Проведен дальнейший анализ влияния коэффициентов предложенной метрики. При рассмотрении полученных результатов было установлено, что учет эмоциональной когерентности необходим для достижения оптимальной производительности метрики.
Emotions dynamically unfold and are jointly constructed throughout social interactions between individuals. Yet, how exactly the experience and expression of emotions interact throughout such interactions remains poorly understood. In this study, we investigated the interplay between the experience and verbal expression of negative affect within and between romantic partners during negative interactions. We examined this interplay in terms of four possible relations: (a) how one's experienced negative affect predicts the verbal expression thereof, (b) how the verbal expression of negative affect predicts a subsequent change in one's own experienced affect, (c) how the verbal expression of negative affect predicts change in a partner's experienced affect, and (d) how one's experienced negative affect predicts the verbal expression of negative affect by a partner. We answered these questions by analyzing second-to-second data of self-reported affect ratings and verbatim transcripts of videotaped negative interactions between romantic partners. Our findings reveal inconsistent evidence for intraindividual relationships between the experience and verbal expression of negative affect. Yet, they demonstrate a consistent, though small, interpersonal relation with the expression of negative affect in one partner predicting the subsequent experience of negative affect in the other. These results suggest that verbal negative emotion expression may be more consistently related to others' experience than one's own, and highlight the role of emotion expression in interpersonal emotion regulation and the social construction of emotional experience, though the small effect sizes suggest this relationship may be subtle and that many other factors contribute to our emotional experiences. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
Understanding the nuances in everyday language is pivotal for advancements in computational linguistics & emotions research. Traditional lexicon-based tools such as LIWC and Pattern have long served as foundational instruments in this domain. LIWC is the most extensively validated word count based text analysis tool in the social sciences and Pattern is an open source Python library offering functionalities for NLP. However, everyday language is inherently spontaneous, richly expressive, & deeply context dependent. To explore the capabilities of LLMs in capturing the valences of daily narratives in Flemish, we first conducted a study involving approximately 25,000 textual responses from 102 Dutch-speaking participants. Each participant provided narratives prompted by the question, "What is happening right now and how do you feel about it?", accompanied by self-assessed valence ratings on a continuous scale from -50 to +50. We then assessed the performance of three Dutch-specific LLMs in predicting these valence scores, and compared their outputs to those generated by LIWC and Pattern. Our findings indicate that, despite advancements in LLM architectures, these Dutch tuned models currently fall short in accurately capturing the emotional valence present in spontaneous, real-world narratives. This study underscores the imperative for developing culturally and linguistically tailored models/tools that can adeptly handle the complexities of natural language use. Enhancing automated valence analysis is not only pivotal for advancing computational methodologies but also holds significant promise for psychological research with ecologically valid insights into human daily experiences. We advocate for increased efforts in creating comprehensive datasets & finetuning LLMs for low-resource languages like Flemish, aiming to bridge the gap between computational linguistics & emotion research.
Psychological research increasingly relies on high-dimensional data, yet it remains challenging to determine whether patterns of representation are independent across experimental contexts. Traditional multivariate approaches, such as decoding, are sensitive to pattern differences but do not directly test factorial hypotheses. In contrast, analysis of variance (ANOVA) provides inferential clarity but is limited to univariate measures. To address this gap, we introduce Multivariate Interaction Classification (MIC), a framework that combines the logic of factorial interaction tests with the sensitivity of multivariate pattern analysis. MIC evaluates representational independence by comparing within-context and cross-context decoding performance. Through simulation studies, we show that MIC reliably distinguishes modality-specific, modality-general, and hybrid representational structures. We then validate the method with affective ratings of gustatory and auditory stimuli, demonstrating how MIC can reveal the coexistence of specific and general codes. By providing a statistically grounded and easily implemented tool, MIC enables researchers to move beyond descriptive decoding toward confirmatory tests of representational hypotheses. All code and materials are openly available to ensure transparency and reproducibility.
BACKGROUND: Attentional bias to cannabis images is posited to drive loss of control over cannabis use and relapse in cannabis use disorder (CUD), but the literature is mixed and limited by inconsistent measurement of CUD and of confounders, including alcohol and nicotine use. This study examined attentional bias in moderate-to-severe CUD (n = 66) compared to controls (n = 42), and its relationship with cannabis/nicotine use, accounting for alcohol use. METHODS: We measured attentional bias using the visual probe task, as the difference in reaction times (RTs) for cannabis versus neutral images, in order to account for individual variability. Linear mixed effect models examined how RTs were affected by (i) group (CUD, control), image type (cannabis, neutral), group-by-image type, and group-by-image type-by-Stimulus Onset Asynchrony (SOA, 200/500 milliseconds) in the whole sample; and (ii) by image type, SOA, and moderators in the CUD group only (i.e., Cannabis Use Disorder Identification Test-Revised [CUDIT-R], subjective craving, arousal/valence ratings of the task's cannabis/neutral images, and nicotine). All models were adjusted for alcohol use. RESULTS: There were no significant group differences in attentional bias. In the CUD group, image type-by-CUDIT-R subgroups differed on RTs (β = -0.748, p =.014), whereby the high-CUDIT-R versus lower CUDIT-R subgroups had significantly faster RTs to cannabis versus neutral images (p =.034, d = -0.10), but this effect did not survive Bonferroni correction for multiple comparisons. No other results were significant. CONCLUSION: Attentional bias might not be a robust feature of CUD, though this notion requires validation in a larger sample using more direct measures of attentional bias.
The article describes intra- and extra-linguistic factors that influence the stability of the onymic space of the Ukrainian language. Intra-linguistic factors are the creation of a certain proper name according to its inherent derivational model; the correspondence of a particular onymic to the formed linguistic norm. Extra-linguistic factors are the level of linguistic, spiritual and political culture of society and its national consciousness. This influence is most pronounced on two classes of onymic vocabulary – anthroponyms and toponyms, as well as on toponymic derivatives – the names of the inhabitants of the corresponding settlement (katoikonyms) and derived adjectives (adjectonyms). It is specially noted of the three-component anthroponymic formula (first name, patronymic, surname) in modern Ukrainian official speech; the elimination of variation in the declension of Ukrainian surnames of the masculine gender with the possessive suffix –ів. Іt is shown that main means of creating katoikonyms in the Ukrainian language are the suffixes –ц-і (plural), –ець, –к-а (singular). Possible functioning of parallel catoikonymic forms with suffixes –ц-і (-ець, –к-а) й –ан-и / –ян-и (-ан-ин, –ан-к-а / –ян-ин, –ян-к-а). The paper deals with problem of establishing derivational models of adjectonyms and their normalization. The author considers that it is necessary to carefully study the historical patterns of the creation and use of adjectonyms in the Ukrainian language, as well as to take into account local traditions. The destructive impact on the Ukrainian oikonymic space of numerous unmotivated ideological renamings of the Soviet era and artificial formations with a Russian-language structure is analyzed. The author shows that it is necessary to cleanse the Ukrainian oikonomika and urbanonymika of such names. Keywords: adjectonyms, anthroponyms, katoikonyms, oikonyms, onymous space, urbanonyms, artificial names (renaming).
With the rise of large language models, service providers offer language models as a service, enabling users to fine-tune customized models via uploaded private datasets. However, this raises concerns about sensitive data leakage. Prior methods, relying on differential privacy within device-cloud collaboration frameworks, struggle to balance privacy and utility, exposing users to inference attacks or degrading fine-tuning performance. To address this, we propose PrivTune, an efficient and privacy-preserving fine-tuning framework via Split Learning (SL). The key idea of PrivTune is to inject crafted noise into token representations from the SL bottom model, making each token resemble the $n$-hop indirect neighbors. PrivTune formulates this as an optimization problem to compute the optimal noise vector, aligning with defense-utility goals. On this basis, it then adjusts the parameters (i.e., mean) of the $d_χ$-Privacy noise distribution to align with the optimization direction and scales the noise according to token importance to minimize distortion. Experiments on five datasets (covering both classification and generation tasks) against three embedding inversion and three attribute inference attacks show that, using RoBERTa on the Stanford Sentiment Treebank dataset, PrivTune reduces the attack success rate to 10% with only a 3.33% drop in utility performance, outperforming state-of-the-art baselines.
INTRODUCTION: The Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) and the Collum-Caput (Col-Cap) concept are tools for clinically assessing cervical dystonia severity. However, the accuracy of human ratings using these scales has not been systematically evaluated due to the lack of objective reference measurements. This study aims to assess and compare the accuracy of human TWSTRS and Col-Cap ratings to evaluate their robustness for clinical and research applications. METHODS: One hundred pictures of 26 avatars mimicking cervical dystonia were created using the Rocketbox Avatar library. Forty-one movement disorder specialists rated the head and neck positioning of the avatars using either TWSTRS or Col-Cap. Movements were defined around two rotational levels (head, neck) in three rotational axes (pitch, yaw, roll). RESULTS: Ratings of angular deviations showed a mean absolute error of 5.8° (SD = 7.0). Rating accuracy was primarily influenced by the magnitude of angular deviation, with larger angles leading to greater estimation errors. Direct comparison of the rating scales revealed a higher accuracy through Col-Cap ratings (71 % vs. 63 % for TWSTRS). Years of clinical experience did not significantly affect rating accuracy. CONCLUSIONS: Both rating systems (TWSTRS and Col-Cap) show moderate accuracy in assessing head and neck positioning from computer-generated avatars, with Col-Cap showing slightly higher overall accuracy but struggling with precise differentiation between head and neck movements. These findings underscore the limitations of current clinical rating scales and highlight the need for more objective, reliable tools to effectively assess cervical dystonia.
Recent advancements in neuroscience have shown that rhythmic motor activity can reduce emotional arousal and support emotion regulation. However, the underlying mechanisms remain unclear; specifically, whether motor rhythm alone is sufficient to produce these effects. To investigate this question, I tested whether engaging in rhythmic hand movements affects emotional reactions to images from the Open Affective Standardized Image Set (OASIS). I conducted three separate experiments comparing participant ratings of images’ arousal and valence when stationary versus when performing a bimanual finger-tapping task. I recruited 110 participants from the Psychology 101 pool at Bates College and convenience sampling methods, between ages 18 and 41 years old. In Experiment 1, participants were divided into three groups (control, simple rhythm, and syncopated rhythm), and I observed evidence that rhythm performance may affect arousal ratings but not valence ratings. So, I conducted Experiment 2 as a follow-up study, simplifying the experimental design by dividing participants into only two groups: control and alternating rhythm. The results of Experiment 2 confirmed the evidence from Experiment 1, but over a limited range of valence ratings. Therefore, I conducted Experiment 3, replicating the procedures of Experiment 2, to evaluate whether my findings generalized to a broader range of valences. Altogether, my findings show that the performance of a bimanual rhythm flattens extreme arousal ratings without affecting valence ratings. This suggests that rhythm performance reduces the subjective intensity of emotional experience, consistent with my hypothesis that rhythmic body movements can contribute to emotional desensitization. This finding suggests that therapeutic approaches involving movements such as EMDR (Eye Movement Desensitization and Reprocessing) therapy may operate by affecting emotional arousal in response to traumatic memories while preserving subjective appraisals of the valence associated with those memories.
Violence has been a significant phenomenon throughout human history and has been studied across various disciplines. With the rise of interdisciplinary approaches, it has become a central issue in many fields. Adapting a psycholinguistic perspective, this study examines how films with violent content influence people’s perception of emotions. It was hypothesized that the violent content in film the participants saw would alter their perception of the emotional content of the words they were shown. To this end, they performed a rating task on a list of positive, negative, and neutral words before and after watching a violent film. A comparison of pre- and post-ratings revealed that valence ratings decreased for all word types after watching the film, while arousal ratings remained unchanged. These findings suggest that exposure to violent content can influence how emotional words are perceived. The results provide valuable insights into the impact of violence on emotional processing.
With the rise of large language models, service providers offer language models as a service, enabling users to fine-tune customized models via uploaded private datasets. However, this raises concerns about sensitive data leakage. Prior methods, relying on differential privacy within device-cloud collaboration frameworks, struggle to balance privacy and utility, exposing users to inference attacks or degrading fine-tuning performance. To address this, we propose PrivTune, an efficient and privacy-preserving fine-tuning framework via Split Learning (SL). The key idea of PrivTune is to inject crafted noise into token representations from the SL bottom model, making each token resemble the $n$-hop indirect neighbors. PrivTune formulates this as an optimization problem to compute the optimal noise vector, aligning with defense-utility goals. On this basis, it then adjusts the parameters (i.e., mean) of the $d_χ$-Privacy noise distribution to align with the optimization direction and scales the noise according to token importance to minimize distortion. Experiments on five datasets (covering both classification and generation tasks) against three embedding inversion and three attribute inference attacks show that, using RoBERTa on the Stanford Sentiment Treebank dataset, PrivTune reduces the attack success rate to 10% with only a 3.33% drop in utility performance, outperforming state-of-the-art baselines.
ObjectiveAssess the patient compliance rate for nasopharyngoscopy as documented in clinical reports.DesignCross-sectional.SettingThirteen cleft teams in North America.PatientsPatients aged 3 to 21 years old with a repaired cleft palate.InterventionsNasopharyngoscopy.Main Outcome MeasurePatient compliance rate for nasopharyngoscopy.ResultsPatient compliance was documented in 128 of 158 reports (81%). Of the 128 reports, patient compliance was reported as "good/excellent" in 65% (n = 83), "marginal/fair" across 10% (n = 13), "poor" in 16% (n = 20), and "other" in 9% (n = 12). Patients with "poor" compliance had lower rates of documented imaging ratings, however 55% (n = 11) of reports included a surgical recommendation.ConclusionsAmong children completing nasopharyngoscopy for velopharyngeal insufficiency surgery planning, at least one-fourth of reports indicate significant compliance issues that limits velopharyngeal port imaging. These findings suggest that some cleft teams and surgeons may be proceeding with surgical management of velopharyngeal insufficiency without adequate visualization or ratings of velopharyngeal anatomy.
Abstract Facial expressions provide rapid and informative cues about others’ emotional and mental states, playing a critical role in social interactions. However, whether distinct emotional expressions reflect discrete neural processes or arise from varying combinations of underlying affective dimensions such as arousal and valence remains a subject of investigation. Crucially, these accounts need not be mutually exclusive: different brain regions may encode emotional expressions along both categorical and dimensional axes to varying degrees. To test this hypothesis, we probed different brain systems involved in emotion recognition - the ventral attentional network and the cortical limbic system centered on the ventromedial prefrontal cortex (vmPFC) - to investigate the extent to which these networks and their subregions encode emotional facial expressions in terms of (1) perceived arousal, (2) arousal+valence, or (3) six discrete emotion categories (anger, disgust, fear, happiness, pain, sadness). To this aim, we modelled the fMRI signal from perceiving movies of facial emotional expressions with ratings for either arousal, arousal+valence or emotion category using representational similarity analysis (RSA) - a method that aims at assessing which ratings model better represents how the perception of facial expressions is reflected by the fMRI parameter estimates across all the voxels within a brain region. This analysis showed that regions in the vmPFC network, including the subgenual cingulate and the medial OFC, are sensitive to ratings for distinct emotion category and for arousal+valence - significantly more so for the former - while they fail to show sensitivity for arousal ratings alone. In the ventral attentional network, the mid-posterior insula showed a similar profile, while the most posterior and ventral insular show evidence of encoding arousal+valence, but not for emotion category or arousal alone. Our findings support the idea that the vmPFC and the mid-posterior insula play a role in encoding emotion-specific and valence representations beyond general arousal processing. These results contribute to understanding how integrative brain regions support emotional empathy and social cognition.
The goal of this research is to provide a new computational framework for analyzing morphological patterns, designed for use in digital philology courseware. There is a computer framework called MorphoScribe, an accessible computer program that utilizes deep learning to identify patterns and segment data based on predefined rules. Using Universal Dependencies (UD) Treebanks makes this possible. MorphoScribe is the parts that make it possible. The software was tested on UD datasets with ten different languages, achieving an average morphological parsing accuracy of 94.2%. The testing that was done made this possible. Another thing to consider is that its precision and recall rates were higher than 93% and 92%, respectively, compared to other products. When it came to the error rates for morpheme boundary recognition, the system was able to lower them by 37% compared to the baseline models. According to the results of educational trials with 120 pupils, parsing activities were finished 32% faster, and morphological analysis abilities were 42% better. It was clear that both changes were for the better. Ninety-five percent of the students who took MorphoScribe's interactive courses reported being satisfied with the platform, as indicated by their responses. The findings presented in this research demonstrate that MorphoScribe not only enhances morphological parsing but also improves the learning experience in digital philology courseware. This is demonstrated by the fact that MorphoScribe helps children learn more effectively.
The article offers a comprehensive analysis of the burlesque metalinguistic communicative personality (BMCP) in contemporary networked discourse, contrasted with the elite metalinguistic communicative personality (EMCP). The object of the study is modern network discourse as an environment for constructing and performing communicative personalities. The subject of the study is the burlesque metalinguistic communicative personality in network discourse, its structural and functional parameters, and the communicative effects arising from them (audience engagement, reframing, delegitimisation/repositioning, etc.) in comparison with EMCP. The purpose of the research is to theoretically conceptualise and empirically model the BMCP phenomenon and to develop criteria for distinguishing it from EMCP. In line with this purpose, the following objectives are formulated: to refine the terminology and definition of BMCP; to identify the theoretical and methodological framework of the study; to describe the structural, linguistic, sociocultural and psychomental characteristics of BMCP; to compare them with the parameters of EMCP; to outline ethical risks (manipulation, hate speech, privacy) and provide recommendations for further research. The empirical data include approximately 40,000 texts from 1,000 accounts across multiple platforms (X/Twitter, Facebook, Instagram, YouTube, Telegram). The study employs a combination of discourse-analytic, pragmalinguistic, context-interpretative, network, and quantitative methods. The findings demonstrate that BMCP represents a new and unstable type of linguistic behaviour that disrupts established cultural codes, employs burlesque, irony, and linguistic chaos, and foregrounds material and globalisation-related factors. This contrasts with EMCP, which fulfils norm-setting and educational functions. The prospects for further research involve expanding the classification of network communicative personalities, modelling their discursive strategies, and analysing the influence of burlesque practices on the formation of new linguistic norms and ethical standards in the digital environment.
OBJECTIVE: Evaluation of facial feminization surgery (FFS) outcomes in the published literature has taken a panfacial perspective. However, horizontal facial thirds analysis may elucidate current deficiencies in facial feminization. In this study, the authors surveyed the general public to determine how FFS influences gender typing of the upper, middle, and lower thirds of the face. METHODS: Standardized frontal and lateral images of isolated horizontal facial thirds from 1 cis -man control, 1 cis -woman control, and 8 consecutive patients before and after FFS were prepared (n = 108 images). Reviewers were asked to determine whether segment images were of a man or woman, indicate their response confidence on a scale from 0 to 10, and specify whether a particular feature influenced their answer. RESULTS: A total of 4182 image ratings were collected from 48 survey respondents. For all 3 segments, there was a significant increase in female gender typing after FFS ( P < 0.001). Male gender typing of the upper segment was significantly higher than the middle and bottom segments regardless of FFS status ( P < 0.001). Confidence in female gender typing increased significantly for only the middle ( P = 0.045) and bottom ( P = 0.041) segments after FFS. Male gender typing of the upper segment after FFS was mostly due to the hairline (49%) or forehead (23%). CONCLUSIONS: Facial feminization surgery increases female gender typing of isolated horizontal facial thirds. However, general public surveys attribute less effective feminization of the upper third to persistent masculine hairlines. Adjuncts such as hair transplantation may further decrease male gender typing after FFS. LEVEL OF EVIDENCE: Level IV.
Large language models (LLMs) are widely deployed in settings where both reliability and efficiency matter. We present a calibrated, seed‑robust empirical comparison of an encoder fine‑tuned model (bidirectional encoder representations from transformers (BERT)‑base) and a decoder in‑context model (generative pre-trained transformer (GPT)‑2 small) across Stanford question answering dataset v2.0 (SQuAD v2.0) and general language understanding evaluation (GLUE)-multi-genre natural language inference (MNLI), Stanford sentiment treebank 2 (SST‑2). Beyond accuracy, we assess reliability (expected calibration error with reliability diagrams and confidence–coverage analysis) and efficiency (latency, memory, throughput) under matched conditions and three fixed seeds. BERT‑base yields higher accuracy and lower calibration error, while GPT‑2 narrows gaps under few‑shot prompting but remains more sensitive to prompt design and context length. Efficiency benchmarks show that decoder‑only prompting incurs near‑linear latency/memory growth with k‑shot exemplars, whereas fine‑tuned encoders maintain stable per‑example cost. These findings offer practical guidance on when to prefer fine‑tuning versus prompting and demonstrate that reliability must be evaluated alongside accuracy for risk‑aware deployment.
The article examines the case forms of nouns in Ulas Samchuk’s novel «Maria» from the perspective of modern linguistic norms. In recent decades, we have observed significant variability in the use of case endings of nouns in the modern Ukrainian language, due to the restoration or activation of many ancient forms. The novel «Maria» by the prominent Ukrainian writer Ulas Samchuk, written in 1933 was chosen exactly for confirmation of the continuity of the grammatical tradition. The proposed study examines the case forms of the dative, accusative, locative, and vocative cases. It was found that the most of the case forms of nouns recorded in the novel are normative even today: the ending of the dative case of masculine nouns -ові, -еві (-єві), which actively displacе the ending -у(-ю), are increasingly penetrating the system of neuter nouns; a significant spread of genitive case forms in the function of the accusative in nouns – names of non-beings; variant forms of the local case (masculine and neuter nouns) in -у and -і in constructions with the preposition по; consistent use of vocative case forms in appeals; alternation of consonants г, к, х in the local and vocative cases. Some case forms of nouns observed in the analyzed novel are used less frequently in modern linguistic practice, while others are qualified as dialectal. It is concluded that the case forms of nouns used in Ulas Samchuk's novel «Maria» reflect the grammatical norms of the Ukrainian language of the first half of the 20th century, many of which were artificially brought closer to the norms of the Russian language in the following decades or relegated to the periphery of the language system due to the socio-political situation. «Ukrainian Spelling» 2019, by bringing back to life some features of the spelling of 1928, renewed the Ukrainian orthographic tradition, which is clearly evidenced by the work of Ulas Samchuk.
This study aims to explore decolonised strategies for challenging hegemonic assessment practices in teacher education, which often equate academic quality with dominant English-language writing conventions. It confronts the perception that effective assessment inherently privileges specific linguistic norms, arguing instead for a social justice approach that reconceptualises evaluation to empower all students. The purpose is to propose how Artificial Intelligence (AI) can be harnessed within a decolonised framework to develop inclusive assessment methods that assess for learning at the Department of Educational Foundations ‘ B.Ed. Honours programme at the University of South Africa. The methodology involved a systematic literature review across major academic databases (ERIC, Scopus, Web of Science, Google Scholar) and specialised journals. Initial searches using terms related to decolonised pedagogy, inclusive assessment, and AI in education yielded approximately 100 papers. After screening titles, abstracts, and full texts for theoretical depth and conceptual relevance, 24 publications were selected for in-depth analysis based on strict criteria of relevance, rigour, and contribution to the synthesis of a decolonised AI approach. The discussion synthesises the literature to argue that AI strategies, when guided by a decolonial ethos, may provide innovative models for supporting academic writing and reframing assessment. This approach may help dismantle repressive structures by moving away from a deficit model and towards one that values diverse student voices and backgrounds. The study recommends that academics intentionally integrate prescribed AI tools into Honours-level assessments to promote learning and equity. The conclusion asserts that a decolonised approach to assessment, augmented by AI, may transform standard practices to advantage historically underrepresented students, ultimately aligning assessment with the goals of social justice and inclusive education.
Stress in interpreting has been well researched over the last few decades. This study takes a multimodal approach to investigate the distinct effects of emotional and cognitive load on interpreter stress. 20 student interpreters consecutively interpreted four first-person mental healthcare narratives from Turkish to English that varied in emotional content and difficulty, within a 2×2 factorial design. Cognitive and emotional responses were captured using galvanic skin response (GSR), prosodic features (pitch and intensity), and three self-report measures (PANAS, STAI, and NASA-TLX). The stimuli were normed using traditional readability indices, expert ratings, and novel natural language processing techniques to control emotional valence and linguistic complexity. The results showed that physiological arousal, as measured by GSR, was primarily driven by cognitive load, particularly during the later stages of interpreting. Emotional load, on the other hand, was more clearly reflected in prosodic markers (especially pitch) and negative affect ratings. The results also hinted at a convergence in pitch between the source speaker and the interpreter. Notably, emotional and cognitive load began to take its toll from the latter stages of the listening phase onwards. However, none of the objective or subjective stress measures predicted interpreting accuracy, suggesting that performance may be mediated by individual coping strategies. The findings are expected to have implications for interpreting pedagogy and the development of cognitive and emotional support strategies in high-stakes interpreting contexts.
Introduction: Neurodegenerative diseases like progressive supranuclear palsy (PSP) present challenges concerning their diagnosis. Neuroimaging using magnetic resonance (MRI) may add diagnostic value. However, modern techniques such as volumetric assessment using Voxel-Based Morphometry (VBM), although proven to be more accurate and superior compared to MRI, have not gained popularity among scientists in the investigation of neurological disorders due to their higher cost and time-consuming applications. Conventional brain MRI methods may present a quick, practical, and easy-to-use imaging rating tool for the differential diagnosis of PSP. The purpose of this study is to evaluate a string of existing visual MRI rating scales and signs regarding their impact for the diagnosis of PSP. Materials and Methods: The population study consisted of 30 patients suffering from PSP and 72 healthy controls. Each study participant underwent a brain MRI, which was subsequently examined by two independent researchers in a double-blinded fashion. Fifteen visual rating scales and signs were evaluated, including pontine atrophy, cerebellar atrophy, midbrain atrophy, aqueduct of Sylvius enlargement, cerebellar peduncle hyperintensities, enlargement of the fourth ventricle (100% sensitivity and 71% specificity) and left temporal lobe atrophy (97% sensitivity and 78% specificity). Conclusions: Enlargement of the Sylvius aqueduct, enlargement of the fourth ventricle and atrophy of both temporal lobes together with the presence of morning glory and hummingbird signs can be easily and quickly distinguished and identified by an experienced radiologist without involving any complex analysis, making them useful tools for PSP diagnosis. MRI visual scale measurements could be added to the diagnostic criteria of PSP and may serve as an alternative to highly technical and more sophisticated quantification methods.
The article offers a comprehensive analysis of the burlesque metalinguistic communicative personality (BMCP) in contemporary networked discourse, contrasted with the elite metalinguistic communicative personality (EMCP). The object of the study is modern network discourse as an environment for constructing and performing communicative personalities. The subject of the study is the burlesque metalinguistic communicative personality in network discourse, its structural and functional parameters, and the communicative effects arising from them (audience engagement, reframing, delegitimisation/repositioning, etc.) in comparison with EMCP. The purpose of the research is to theoretically conceptualise and empirically model the BMCP phenomenon and to develop criteria for distinguishing it from EMCP. In line with this purpose, the following objectives are formulated: to refine the terminology and definition of BMCP; to identify the theoretical and methodological framework of the study; to describe the structural, linguistic, sociocultural and psychomental characteristics of BMCP; to compare them with the parameters of EMCP; to outline ethical risks (manipulation, hate speech, privacy) and provide recommendations for further research. The empirical data include approximately 40,000 texts from 1,000 accounts across multiple platforms (X/Twitter, Facebook, Instagram, YouTube, Telegram). The study employs a combination of discourse-analytic, pragmalinguistic, context-interpretative, network, and quantitative methods. The findings demonstrate that BMCP represents a new and unstable type of linguistic behaviour that disrupts established cultural codes, employs burlesque, irony, and linguistic chaos, and foregrounds material and globalisation-related factors. This contrasts with EMCP, which fulfils norm-setting and educational functions. The prospects for further research involve expanding the classification of network communicative personalities, modelling their discursive strategies, and analysing the influence of burlesque practices on the formation of new linguistic norms and ethical standards in the digital environment.
This article explores the peculiarities of how euphemistic and dysphemistic expressions function as tools in the author’s strategy of linguistic play within the narrative space of contemporary French writer Bernard Werber’s short prose. The research is based on the collection “L’Arbre des possibles et autres histoires”, where each story unfolds as a speculative scenario set in an alternative spatiotemporal dimension. Linguistic play is understood as a deliberate deviation from linguistic norms, a playful manipulation of linguistic resources to achieve a particular pragmatic resonance. In Bernard Werber’s works, euphemistic and dysphemistic substitutes manifest as linguistic entities drawn from diverse discursive domains – media, administrative, juridical, and scientific domains, particularly medical, biological, psychological, and philosophical, as well as lexemes and expressions from diverse linguistic registers interacting within a single context. Such stylistic heterogeneity, coupled with the contamination of usual media euphemisms with substitutes forged through alternative stylistic figures, generates not merely humorous, ironic, and satirical effects, but also cultivates an atmosphere of absurdity and, occasionally, cognitive dissonance within the reader’s consciousness. The synthesis of euphemisms and dysphemisms within unified contextual boundaries precipitates an effect of semantic and stylistic flickering – a peculiar oscillation between the veiling and illumination of an object’s negative attributes in a 'veil–spotlight' mode (in line with D. Jamet’s metaphors). This phenomenon induces cognitive tension in the reader, thereby activating their interpretative engagement. Through this mechanism, the author not only constructs possible worlds but also engages the reader in an active game of meaning decipherment – a manifestation that simultaneously embodies postmodernist literary practice and reflects the distinctive features of the writer’s individual stylistic signature.
This paper presents a critical examination of how linguistic diversity, social identity, and equity intersect to shape inclusion within modern, multicultural, and technologically mediated societies. The primary aim is to uncover the complex dynamics through which language serves both as a bridge to empowerment and a barrier to participation. Adopting a conceptual and interdisciplinary methodology, the study synthesises scholarship from linguistics, education, digital technology, and governance to develop a comprehensive framework that situates multilingualism as central to advancing fairness and social justice. The analysis reveals that language functions as a powerful marker of identity and belonging, mediating access to opportunities, resources, and representation. Through an intersectional lens, the study demonstrates that linguistic hierarchies frequently interact with social structures of power, such as class, gender, and ethnicity, to reproduce or challenge inequality. Multilingualism, when embraced within institutional, educational, and digital contexts, emerges as a transformative tool that promotes inclusion, empathy, and cultural understanding. However, the persistence of dominant linguistic norms continues to marginalise minority voices, reinforcing asymmetries of knowledge and power within global communication systems. The findings affirm that the equitable integration of multilingual and intersectional frameworks is essential for realising sustainable social progress. The study recommends that policymakers and educators adopt inclusive language strategies that reflect the realities of linguistic diversity while ensuring ethical and culturally responsive communication in both physical and digital spaces. Collaboration among linguists, technologists, and institutional leaders is further advocated to construct systems that prioritise linguistic justice as a foundation for democratic participation and equity. By reframing language as a medium of empowerment and transformation, the research contributes meaningfully to the global discourse on identity, inclusion, and social justice in the twenty-first century.
The present research assessed university student stakeholders’ perceptions of positive outcomes (i.e., appropriateness and benefits of conferencing) and negative outcomes (i.e., endangerment and revictimization of the complainant) associated with restorative justice-based direct conferencing in sexual misconduct cases. Stakeholders received random assignment to a 2 (allegation severity: more vs. less) × 2 (evidence strength: lower vs. higher) between-participant experimental design. More severe allegations and higher evidence strength were associated with lower ratings of appropriateness; allegation severity and evidence strength interacted to affect ratings of benefits; and more severe allegations, but not stronger evidence, were associated with higher ratings of endangerment and revictimization. Belief in the alleged perpetrator’s guilt explained the relationship between evidence strength and ratings of appropriateness, and desire to punish the alleged perpetrator explained the relationship between allegation severity and ratings of appropriateness. Researchers and Title IX coordinators should evaluate and respond to stakeholder sentiment toward direct conferencing.
PURPOSE: This study interrogates the intercultural experiences of African international students (AIS) in a US Midwestern University. With a focus on West African students, the study explores how students confront and overcome linguistic, cultural, and systemic barriers as they create new dynamic spaces which are neither American nor African, but a mixture of both. SUBJECTS: 12 West African students. METHODS: I utilized ethnographic observations and in-depth interviews to better understand how the AIS engage in ongoing identity negotiation through language, food, cultural expressions, and technology. I conducted 40 hours of participant observation research over a 5-month period and interviewed 12 West African students. OUTCOME: Drawing on the theoretical constructs of hybridity and language ideology, the findings reveal how AIS negotiate identity by using traditional African cultural traits, rooted in what I term Africanism—with new cultural influences, generating unique, fluid and constantly-evolving hybrid identities. Language emerges as a powerful site of hybridity, where students shift between linguistic norms to balance intelligibility and cultural authenticity. These hybrid identities emerge through active, creative processes. IMPACT: The findings offer important implications for higher education, emphasizing the need for more culturally responsive support systems that recognize the distinct experiences of African students. Additionally, the study contributes to the fields of communication and migration studies by advancing a nuanced understanding of identity formation in transnational contexts. By foregrounding the voices of African students, this research challenges monolithic representations of international students and promotes intercultural dialogue in fostering inclusive communities and enriching educational environments.
В статье проводится анализ способов передачи новых слов и выражений, которые появились во время пандемии новой коронавирусной инфекции, с английского и немецкого языков на русский язык. В исследовании представлены структурно-семантическая и функциональная характеристики 50 неологизмов английского языка и 50 неологизмов немецкого языка, отобранных методом сплошной выборки из текстов онлайн-СМИ на английском и на немецком языках. В результате исследования была выявлена доминирующая роль калькирования, а также растущая частотность транскрибирования и транслитерации, что свидетельствует о стремлении к оперативной и точной передаче смысла новых понятий в российских СМИ. Наблюдаемая толерантность к неологизмам иноязычного происхождения, сохраняющим оригинальную форму, говорит о либерализации языковых норм и готовности носителей русского языка к принятию иноязычных элементов. Заимствование аналитической модели словообразования, свойственной английскому языку, указывает на влияние последнего на языковую систему русского языка. Меньшее количество неологизмов, переведенных с немецкого языка, по сравнению с английским, вероятно, отражает глобальную роль английского как лингва франка в сфере науки, технологий и международных коммуникаций. The article analyses the ways of translating from German and English into Russian the new words and phrases which emerged during the coronavirus pandemic. It presents the structural, semantic, and functional characteristics of 50 English neologisms and 50 German neologisms, selected by the continuous sampling method from online media in both languages. The study reveals the dominant role of calquing, as well as an increasing frequency of transcription and transliteration, indicating a desire for quick and accurate transmission of the meaning of new concepts in the Russian media. The tolerance for borrowed neologisms that retain their original form suggests a liberalisation of linguistic norms and a willingness to incorporate foreign language elements. The borrowing of the analytical word formation model typical of the English language indicates the influence of this language on the linguistic system of Russian. The smaller number of neologisms translated from German, compared to English, probably reflects the global role of English as a lingua franca in science, technology, and international communication.
The research paper is devoted to a comprehensive consideration of the mediative function of language in traditional Kazakh culture. The main purpose of the work is to identify the specifics of the use of language in the historical and cultural practice of Kazakh society as a means of coordinating interests, settling disputes and harmonizing social relations. In accordance with this goal, the article defines the following tasks: cultural and social foundations of linguistic mediation in traditional Kazakh society; description of linguistic structures and pragmatic strategies of institutions that ensure dispute resolution through speech; identification of semantic features of linguistic norms aimed at maintaining social harmony in national culture. In the course of solving these tasks, the oral oratorical heritage of the Kazakhs is analyzed from linguistic and pragmatic positions. The conducted research allowed us to establish that the mediative function of language in traditional Kazakh culture goes beyond simple communication. It forms the basis of mechanisms for maintaining social harmony, regulating the moral code of the community and the peaceful settlement of conflict situations. It is also proved that the speech culture of leaders and bi-speakers contributed to the formation of a specific national model of mediation, and its language strategies (forms of etiquette, indirect ways of expression, metaphorical and symbolic structures and pragmatic means of mitigation) are consonant with modern theories of mediation. The scientific significance of the study lies in the fact that the phenomenon of mediation in Kazakh culture is being systematically examined for the first time in a linguistic and pragmatic perspective, which makes it possible to identify the contribution of traditional speech experience to the development of the general theory of mediation. The practical value of the work is determined by the possibility of using the results obtained in mediation training programs, in the development of ethno-cultural models of negotiation practices, as well as in projects aimed at updating the culture of Kazakh oral speech.
The article discusses the process of compiling the first corpus of errors in contemporary Polish and its possible applications. The main goal of the corpus was to use it to train language models based on deep neural networks. However, during the annotation, several problems that may be of interest to linguists (especially those involved in prescriptive linguistics) were identified. Difficulties in annotation suggest that the very concept of error is unclear, as is categorization of language errors. Corpus statistics give an approximate picture of how well educated Poles know the linguistic norm and what types of errors most commonly appear in texts. Such information can be used for the purpose of language education at the school level and in Polish studies. Keywords Polish; language errors; typology of errors; corps; linguistic norm
Abstract Male reproductive success varies within populations. Models explaining reproductive skew largely emphasize dominance, yet the determinants of male reproductive success in egalitarian societies remain poorly understood. Using nine years of behavioral and genetic data from five parties, we investigated male reproductive monopolization in wild Guinea baboons ( Papio papio ), an egalitarian multilevel society with one-male “units” nested within “parties” and low male-male contest competition. Genetic analyses showed 93% of the 71 infants were sired by the female’s “unit male”, with rare extra-unit paternities consistent with limited control models. Within parties, reproduction was shared among multiple unit males, resulting in low reproductive skew, with top males siring 23-40% of offspring, well below levels in hierarchical species. Female takeovers were rare, suggesting male restraint. Reproductive success, assessed as the number of unit females, was positively associated with dominance ratings, yet males with average ratings held the largest units. Prime-age predicted reproductive success better than dominance. In conclusion, male Guinea baboons’ reproductive success is shaped less by dominance than by age and stable associations with females, who play an active role in inter-sexual relationships. These results emphasize the need to move beyond frameworks focused solely on dominance-based mechanisms and to consider species-specific social organization. Significance statement Current theoretical models of male reproductive success often focus on dominance and males’ ability to monopolize access to females. Yet, less is known about the determinants of reproductive success in societies with egalitarian male relationships, such as the multilevel society of wild Guinea baboons. Using nine years of behavioral and genetic data from five groups (“parties”), we show that unit males almost always sire their unit’s offspring. However, at the party level, male reproductive skew is low. Reproductive success is better predicted by prime-age than dominance and appears to result from long-term associations with females, likely shaped by female choice. Our results highlight the need to examine diverse social systems to understand the evolution of reproductive strategies beyond dominance-centered models.
Background and objective Navigating interprofessional team dynamics is essential for high-quality patient care in pediatric settings. This study involved medical students on a pediatric clerkship who explored the characteristics of high- and low-performing clinical teams by considering drivers and barriers to effective team performance. By analyzing these reflections, the study aimed to identify key facilitators and barriers to effective team-based care. Methods Survey evaluations and narrative reflections were completed by third-year students (M3s) at a single US allopathic medical school during their pediatric clerkship after receiving training in TeamSTEPPS® and Institute for Healthcare Improvement (IHI) Open School, two programs that support quality improvement (QI) in healthcare. Descriptive statistical and inductive thematic analyses were conducted on the resulting 183 narratives. A valence rating system was employed to quantify narrative responses as positive/attractive or negative/aversive, with a Cronbach alpha of 0.958 between two independent reviewers. Results Inductive thematic analysis generated 40 themes that we grouped under the five TeamSTEPPS® skill domains (situation monitoring, communication, leadership, team structure, mutual support) into thematic conceptual models. High-performing teams demonstrated open communication, role clarity, shared understanding, and organized task delegation. Low-performing teams displayed a lack of information exchange, uncertain team roles, unhealthy power dynamics, and disorganized task delegation. Conclusions After instruction in QI methods, pediatric clerkship students identified consistent drivers of and barriers to effective team performance. The themes within the narrative reflections can provide insights into improving patient care delivery, specifically around situation monitoring, communication, and team structure.
R) subunit regulation in altering sensitivity to neuroactive steroids, a theory difficult to test in humans. We examined peripheral mRNA expression of GABRD, GABRG2, and GABRA5 in a transdiagnostic sample of 112 participants (75 psychiatric outpatients with suicidality; 37 controls) to capture a dimensional range of MCAC severity. Participants provided daily affect ratings and five LH-timed blood samples using PAXgene tubes for RT-PCR analysis. Replicating preclinical findings, GABRG2 expression decreased during the midluteal phase. Critically, individual differences in both person-mean expression and perimenstrual change (AUCi) were correlated with cyclical symptom trajectories. Greater perimenstrual increases or higher average expression were associated with classic luteal-phase worsening of anxiety (GABRG2, GABRA5), irritability (GABRG2), and suicidal ideation (GABRG2, GABRA5, GABRD). However, relative to those with moderate or stable expression, perimenstrual decreases or lower average expression were also linked to cyclical worsening of anxiety and suicidal ideation that emerged around menses and persisted into the follicular phase. Overall, more stable subunit expression was associated with less cyclical symptom change. These findings could be consistent with a loss of homeostatic stability in GABAergic plasticity, rather than a simple unidirectional differences in levels or changes, as a potential correlate of MCAC. Peripheral GABAAR gene expression warrants further investigation, particularly in experimental studies, to determine whether there are causal associations between subunit gene expression and MCAC.
BACKGROUND: Mild manifestations of individual cerebral small vessel disease (CSVD) markers are common and may not denote increased risk, but high CSVD burden identifies individuals at increased risk of stroke and dementia. Scores incorporating multiple individual CSVD markers may better identify a person's risk. We related a multimarker CSVD score to risk of incident stroke and compared it with the Framingham Stroke Risk Profile (FSRP) in community-dwelling individuals. METHODS: Framingham Heart Study participants aged ≥55 years, free of stroke and dementia, and with brain magnetic resonance imaging ratings of CSVD markers were included. A multimarker CSVD score reflecting increasing CSVD burden was used, assigning 1 point each for presence of cerebral microbleeds, severe perivascular spaces, extensive white matter hyperintensities, covert brain infarcts, and cortical superficial siderosis. Multivariable Cox proportional hazards regression analyses were used to relate CSVD score to incident stroke. RESULTS: Among 1154 participants (46% men, mean age 70.9±8.7 years), 92 (8%) developed stroke over a median follow-up of 8.6 years (Q1-Q3: 5.1-12.5). In models adjusting for age, sex, time interval between clinic exam and magnetic resonance imaging, Framingham cohort, and FSRP, those with ≥3 markers had increased risk of stroke (hazard ratio [HR], 2.62 [95% CI, 1.17-5.88]). In comparison, a 5% increase in FSRP was also associated with increased risk (adjusted HR, 1.16 [95% CI, 1.04-1.29]). The FSRP and CSVD score had similar model discrimination metrics. CONCLUSIONS: Higher CSVD burden is associated with increased risk of stroke, beyond the effect explained by risk factors in the FSRP. These findings support consideration of CSVD burden to identify risk of stroke in community-dwelling individuals for early implementation of preventive strategies.
Large Language Models (LLMs) employ deep learning algorithms to generalize patterns in data. Applying these LLMs to classification tasks can reduce the required labor and time. The research aims to fine-tune the LLM Llama 3.1 to correctly identify whether a chosen text message exhibits a positive or negative emotion. The goal of this procedure is to apply the fine-tuned LLM to large databases of text messages and locate users whose recent texts contain a large proportion of negative samples. This way, I can alert the users and direct them to help very early on. I chose the Stanford Sentiment Treebank v2 (SST-2) dataset. It mimics the emotional polarity of real texts with its even positive-negative sample distribution and its contextless format. I used the Unsloth framework and LoRa to significantly reduce the resources required during the fine-tuning process. I tested the model by taking SST-2’s train split and inputting them individually into the trained model. Using this method, I found the Llama model to be highly accurate, with an accuracy of 94.8%. Interestingly, it had a high average Binary Cross-Entropy (BCE) Loss of 0.782 but achieved high accuracy. The testing against other models shows that the BCE Loss for sentiment analysis is not correlated to the actual accuracy of the model. From the results, I determined Llama 3.1 was the most suitable LLM for the sentiment analysis of large text databases.
Zora Neale Hurston’s Their Eyes Were Watching God presents a radical departure from the tragic mulatta trope in African American literature by centering a black feminist protagonist, Janie Crawford, who is neither defined by racial ambiguity nor constrained by the moral expectations imposed on middle-class black women of the 19th and early 20th centuries. Unlike her literary predecessors, Janie speaks in black vernacular, embraces her sexuality, and ultimately finds agency outside of marriage, despite the novel’s exploration of love and relationships. This paper argues that Hurston’s portrayal of Janie’s three marriages illustrates a pessimistic view of black women’s status within love and marriage, revealing that even true love cannot fully liberate them from patriarchal constraints. Through an analysis of Janie’s relationships, this paper demonstrates how Their Eyes Were Watching God challenges intra-community sexism and critiques the internalization of white patriarchal values by black men. Additionally, it explores Hurston’s literary innovations, particularly her use of black dialect and folklore, as an intervention against white literary standards and a foundation for later black feminist narratives. Hurston’s use of black dialect and folklore functions not merely as a literary gesture, but as a deliberate political and aesthetic intervention. The black vernacular, often seen as non-literary or even “primitive” in dominant white and even black literary standards, becomes in Hurston’s hands a medium of authenticity, resistance, and empowerment. By embedding Janie’s voice within this dialect—particularly through her dialogues with other women and her defiance of male authority—Hurston decentralizes white linguistic norms and reclaims black southern oral traditions as legitimate literary forms.By foregrounding the singularity of Janie’s experience, Hurston’s novel marks a turning point in the representation of black women in literature, paving the way for subsequent authors like Alice Walker and Toni Morrison to further explore Black female autonomy and agency.
The ubiquity of digital technologies has increased assessments of thoughts, behaviors, and experiences via electronic devices. Surveys on smartphones or laptops often implement Visual Analogue Scales (VAS), recording responses on a continuous slider (0-100). This is particularly relevant for data collection in daily life, such as ecological momentary assessments (EMA), which repeatedly present items on mobile devices. However, the accuracy of digital VAS has been questioned, particularly regarding tactile precision (e.g., ability to accurately select values) and the consistency of scale interpretation both between- and within-persons over time (e.g., change in scale interpretation or reactivity to repeated measures). Participants (N = 3,761, 67.03% female; Mage = 47.09; SD = 14.41) from the Critical Incidents and Psychological Adaptation (CIPA) Study completed a 30-day EMA assessment. We investigated the accuracy of VAS in terms of (1) tactile precision, (2) respondents’ perception of the neutral point post-EMA, and (3) test-retest consistency of affect ratings pre- and post-EMA. (1) Tactile precision was assessed by asking participants to enter exactly 31 on a 0-100 slider. Results showed high precision (M = 31.01; SD = 3.28; 87.0% scored between 30-32). (2) Between-person agreement on scale perception was assessed by asking participants to determine the neutral score on two affect items (unipolar and bipolar). 82.19% and 88.89% indicated the expected scale midpoint (50 and 0, ± 5) as neutral, respectively. Neutral points deviating from the expected midpoint were correlated (r =.71-.73) with the person-specific means across the EMA period on the respective item. (3) Test-retest consistency was evaluated by asking participants to rate how happy/sad they/others would rate affective events (e.g., a serious argument) pre- and post-EMA. Consistency across time was high (median change = 0-5). Findings support the accuracy and consistency of digital VAS, within the scope of the current methods.
Timbral blend is a phenomenon that occurs when two or more concurrent acoustic events produced by distinct sources fuse perceptually and give rise to new timbres. Auditory scene analysis proposes that concurrent grouping cues of onset synchrony, harmonicity, and parallel change in pitch and dynamics are involved in the perceptual fusion of events, but research has also shown that several timbral cues can affect concurrent grouping. We investigated potential factors that may cause different degrees of instrumental blend in orchestral excerpts using rating scales ranging from “unity” to “multiplicity” and from “strongly blended” to “not at all blended.” With linear mixed effects modeling, the factors found to affect ratings included the rating scale used, musical training, timbre class (instrument families involved), the degree of parallelism and onset synchrony of melodic lines involved in the blend, the number of different notes present simultaneously, and several acoustic features related to timbre. Musicians differ from nonmusicians in the use of the multiplicity scale, rating excerpts as more multiple, even if they are fairly well blended, whereas nonmusicians ratings are similar for both scales and to musicians’ ratings of blend. Excerpts with bowed strings and/or woodwinds blend the strongest, followed by combinations involving brass instruments, with excerpts involving percussion and plucked strings blending the least. The important finding of this study on real musical excerpts is in demonstrating the relative roles of the score-based and acoustic factors that are associated with the perception of multiplicity and blend in complex orchestral sonorities as well as the influence of musical training.
This article examines ethnocultural identity in the artistic space of the postcolonial novel from the perspective of literary translation. Based on Abraham Verghese’s “Cutting for Stone” and its Russian translation by S. Sokolov, the study identifies and systematizes the specific difficulties involved in rendering the linguistic markers of identity. The object of the research is the linguistic and stylistic means of expressing ethnocultural identity in the original text of the novel, while the subject comprises the strategies and methods for translating these elements into Russian. The central argument is that recognizing the distinct genre-stylistic conventions of postcolonial literature is essential for developing effective translation strategies and achieving textual adequacy. The research employs a comprehensive methodological approach, integrating semantic, contextual, comparative, stylistic and translation analysis. The analysis reveals that the most significant challenges for a translator are posed by passages conveying cultural and linguistic polyphony, hybridity, and the fundamental oppositions that shape both character identity and the text’s conceptual space. The study highlights how elements such as foreign-language inclusions, erratives, graphons, and other deviations from linguistic norms act as manifestations of linguistic and cultural hybridity and as means of character self-identification. Furthermore, culture-specific items (realia) are used not only to embody cultural memory but also often acquire metaphorical and symbolic meanings, highlighting central narrative conflicts and functioning as markers of individual and collective identity. The novelty of this research lies in its endeavor to formulate practical recommendations for more authentically recreating the effects of linguistic and cultural hybridity and internal identity conflict in translation. The study concludes that while translating culturally marked units in a postcolonial novel, it is essential to consider its genre specificity, thematic and ideological content and macro-context.
The ability to complete sentences and guess words is an essential component of NLP. Virtual assistants and advanced technology that dictate text to humans both gain from them. Because of their ambiguity, reliance on long-term events, and lack of information, humans are currently unable to complete these jobs. Common systems use RNNs and transformer topologies; however, these models may struggle to understand and identify uncommon words. Such limitations may hinder their productivity and make it more difficult for them to put their knowledge into practice. To overcome these challenges, this work employs federated learning for word guessing and N-gram modelling. To identify linguistic trends that manifest in many locations, the federated N-grams approach makes use of a large number of computers. Both bias and missing data can be reduced in this way. The Penn Treebank and AS WIKI-text are two of the basic datasets that we use to train and evaluate our model. Using optimisation techniques, we can understand the situation, handle unusual words, and account for bias in our guesses. The results of this study show that Fed-based learning has the potential to revolutionise language modelling by removing control mechanisms from AI while simultaneously protecting users' personal information. With the use of the N-grams method, predictions regarding the next word could be more precise and contextually aware.
This article explores the requirements for corpus compilation within the GiesKaNe project (University of Giessen and Kassel, Syntactic Basic Structures of New High German). The project is defined by three central characteristics: it is a reference corpus, a historical corpus, and a syntactically deeply annotated treebank. As a historical corpus, GiesKaNe aims to establish connections with both historical and contemporary corpora, ensuring its relevance across temporal and linguistic contexts. The compilation process strikes the balance between innovation and adherence to standards, addressing both internal project goals and the broader interests of the research community. The methodological complexity of such a project is managed through a complementary interplay of human expertise and machine-assisted processes. The article discusses foundational topics such as tokenization, normalization, sentence definition, tagging, parsing, and inter-annotator agreement, alongside advanced considerations. These include comparisons between grammatical models, annotation schemas, and established de facto annotation standards as well as the integration of human and machine collaboration. Notably, a novel method for machine-assisted classification of texts along the continuum of conceptual orality and literacy is proposed, offering new perspectives on text selection. Furthermore, the article introduces an approach to deriving de facto standard annotations from existing ones, mediating between standardization and innovation. In the course of describing the workflow the article demonstrates that even ambitious projects like GiesKaNe can be effectively implemented using existing research infrastructure, requiring no specialized annotation tools. Instead, it is shown that the workflow can be based on the strategic use of a simple spreadsheet and integrates the capabilities of the existing infrastructure.
This paper explores worldwide researchers’ perspectives on e-learning policy in higher education, focusing on an approach that examines English-language and Chinese-language literature, which elucidates the intricate nature of e-learning policy. Despite a number of studies on e-learning policy in higher education context, few have considered using two linguistic databases (English and Chinese) for evaluating global perspectives on e-learning policy. To make sure that the review was composed systematically, the preferred reporting items for systematic reviews and meta-analysis was utilized, four English (Scopus, ERIC, Google Scholar, &amp; SAGE) and three Chinese databases—China National Knowledge Infrastructure, the Chinese University of Hong Kong Library, and Airiti Library—were used to screen pertinent studies for analyzing. Authors reached a consensus on coding 60 studies into six categories, which encompass perceptual, portraying, theory, literature reviews, comparative study, and discourse analysis. The review reveals a clear focus on theoretical articles in both English- and Chinese-language literature, with these articles being the most common across six segments. To further explore emerging trends on e-learning policy research, three primary themes and 19 sub-themes are identified from 60 studies. Implications for advancing future research are outlined.
Abstract In social interactions, we often encounter situations where a partner’s face is (partially) occluded, e.g., when wearing a mask. While emotion recognition in static faces is known to be less accurate under such conditions, we investigated whether these detrimental effects extend to empathic responding, mentalizing (i.e., Theory of Mind), and prosociality in more naturalistic settings. In four studies (N total = 157), we presented short video clips of narrators recounting neutral and emotionally negative autobiographical stories, with their faces shown in four conditions (two per experiment): fully visible, eyes covered, mouth covered, and audio-only. Participants then responded to questions assessing affect, mentalizing performance, and willingness to help. Affect ratings were slightly lower when the narrator’s mouth was covered, and participants were less willing to help narrators with covered eyes. Importantly, however, empathic responding and mentalizing performance remained robust across visibility conditions. Thus, our findings suggest that social understanding – specifically, empathizing and mentalizing – is not substantially impeded by partial or complete facial occlusion, when other cues, such as vocal information, can be used to compensate. These insights may help contextualize concerns about detrimental effects of face coverage in social interactions.
Effective Aanaphora Resolution (AR) is essential for computational linguistics, as it underpins coherent text analysis, information processing pipeline, and the development of advanced language technologies. Pronominal Anaphora Resolution plays a crucial role in analyzing and understanding large text collections, enabling discourse understanding systems and enhancing the performance of related applications like text summarization, sentiment analysis, and machine translation. This paper proposes and evaluates a novel hybrid method for Hindi AR. The proposed method uses the rule-based method to resolve reflexive and locative pronouns, whereas it uses supervised classifiers to resolve demonstrative and relative pronouns. We investigate two machine learning and one deep learning classifiers- the Distributed Random Forest classifier, Stacked ensemble classifier, and Multi-Layer Perceptron. The performance evaluation is done on two standard datasets: the Hindi tourism dataset and the Hindi Dependency Treebank Data (HDTB). The stacked ensemble model outperforms all other models investigated in this paper on the Hindi Tourism dataset with an accuracy of 76.33%. The deep learning model performs better than stacked ensemble and random forest on the HDTB dataset with an overall accuracy of 75.96%. The proposed hybrid method outperforms most of the earlier reported work on Hindi AR. This research demonstrates the potential of DL and ML classifiers in developing an automatic entity linking system in Hindi text, which is necessary for the correct semantic interpretation of text..