Aug 2026· Frontiers in Communication· 0 citations· 32 references
Abstract
This study presents the first systematic literature review of research on Estonian Sign Language (EVK) published between 1985 and 2024.
Using Okoli's eight-step systematic literature review (SLR) model, we identified and screened 52 sources. Twenty-one were excluded on the basis of predefined criteria, leaving 31 studies for analysis. We examined how data-collection methods, annotation practices, and analytical frameworks have shaped the development of EVK linguistics.
The findings reveal a gradual shift from descriptive documentation toward more empirical and methodologically reflective research. Many studies reflect the methodological conditions of their time, including a reliance on elicited rather than naturalistic data, non-standardized transcription practices until 2006, and analytical frameworks shaped by earlier linguistic traditions of the 1960s–2000s. Across the reviewed literature, methodological conditions varied considerably, reflecting different historical phases in which formal training opportunities in linguistics, research teams with EVK proficiency and corpus-based infrastructures were not yet consistently available. Only a small number of studies reported the use of ELAN annotation or systematically documented participatory practices involving Deaf collaborators or heritage signers.
The review highlights significant progress in methodological awareness but underscores persistent gaps in the Estonian Sign Language research landscape, linguistic infrastructure and involvement of Deaf researchers. By tracing EVK research across historical phases, the review identifies priorities that could inform a collaborative, ethically grounded and corpus-based research agenda. Such an agenda could support cooperation among universities, Deaf community organisations, researchers, educators and language-policy actors, while enhancing data transparency, reproducibility and the sustainable development of EVK research.
English has become the dominant language of communication in sport sciences, yet many students continue to experience difficulties in acquiring specialised vocabulary, technical terminology, and discipline-specific discourse required for academic and professional contexts. Although corpus linguistics offers a robust evidence-based approach for identifying authentic language patterns, its pedagogical application in sport sciences remains fragmented. This systematic literature review synthesises empirical evidence on corpus-based English research relevant to sport sciences education. The review followed the PRISMA 2020 guidelines and searched the Scopus database because of its comprehensive coverage of peer-reviewed interdisciplinary literature in linguistics, education, and sport sciences. A structured Boolean search identified 403 records, of which 18 studies (2015–2025) met the predefined eligibility criteria after duplicate removal, title and abstract screening, and full-text assessment based on the PICOS framework. Two independent reviewers conducted the screening and data extraction process, with disagreements resolved through discussion. Methodological quality was evaluated using the FICO framework, and evidence was synthesised through the thematic synthesis approach proposed by Thomas and Harden (2008). Four overarching themes emerged: (1) specialised vocabulary development and word-list construction, (2) sport terminology and cross-linguistic variation, (3) spoken and instructional discourse in sport contexts, and (4) corpus-informed pedagogical applications. The findings indicate that technical vocabulary and multiword expressions constitute a substantial proportion of sport science discourse, while inconsistencies in terminology across languages continue to challenge instructional standardisation. Nevertheless, most existing studies remain descriptive, with relatively few evaluating instructional effectiveness or learning outcomes. This review provides a comprehensive synthesis of corpus-based evidence and highlights the need for discipline-specific teaching materials, corpus-informed curricula, and intervention studies evaluating pedagogical effectiveness. The findings offer practical guidance for English for Specific Purposes (ESP) curriculum development and establish directions for future corpus-based research in sport sciences education.
Kristian Burhan, I. Ifdil, Rudyanto Rudyanto· Lentera Negeri· 0 citations
This study maps the emerging themes and methodological approaches characterizing translation and interpreting (T&I) research published by Indonesian-affiliated authors between 2020 and 2024. A systematic mapping review design was adopted, drawing on Grant and Booth (2009) and Cooper (2016), with data collected from Google Scholar via Publish or Perish and GARUDA using bilingual keyword clusters. Following systematic screening of 1,453 deduplicated records, 753 publications were included in the final corpus. Findings reveal four dominant thematic clusters: machine translation quality evaluation, audiovisual translation, translation techniques and strategies, and cultural and literary translation. Interpreting studies account for less than 9% of the corpus, reflecting not merely a research gap but an institutional failure to nurture interpreting as a distinct academic and professional discipline in Indonesia. Qualitative textual analysis dominates methodologically, with an emerging computational strand concentrated in internationally indexed outlets. The study provides a preliminary baseline map of accessible Indonesian T&I journal literature, with implications for researchers, educators, professional bodies, and policy makers seeking to strengthen the field’s scholarly infrastructure.This study maps the emerging themes and methodological approaches characterizing translation and interpreting (T&I) research published by Indonesian-affiliated authors between 2020 and 2024. A systematic mapping review design was adopted, drawing on Grant and Booth (2009) and Cooper (2016), with data collected from Google Scholar via Publish or Perish and GARUDA using bilingual keyword clusters. Following systematic screening of 1,453 deduplicated records, 753 publications were included in the final corpus. Findings reveal four dominant thematic clusters: machine translation quality evaluation, audiovisual translation, translation techniques and strategies, and cultural and literary translation. Interpreting studies account for less than 9% of the corpus, reflecting not merely a research gap but an institutional failure to nurture interpreting as a distinct academic and professional discipline in Indonesia. Qualitative textual analysis dominates methodologically, with an emerging computational strand concentrated in internationally indexed outlets. The study provides a preliminary baseline map of accessible Indonesian T&I journal literature, with implications for researchers, educators, professional bodies, and policy makers seeking to strengthen the field’s scholarly infrastructure.
This study integrates bibliometric visualization with content coding to clarify the development, knowledge structure, and research gaps of digital discourse studies, offering directions for future interdisciplinary inquiry.
Yifan Liu, Omar Ali Al-Smadi, Siti Soraya Lin Abdullah Kamal et al.· Journal of Nusantara Studies...· 0 citations
A review of journal articles published between 2020 and 2024 that discuss the design, effectiveness, perception, and lexicographical quality of Arabic digital dictionaries identifies research gaps regarding the integration of artificial intelligence and deeper linguistic features into Arabic digital dictionaries.
Nadela Yusrizal, Asep Sopian, Mia Nurmala· 0 citations
Poetry is a unique form of expression valued for its role in preserving cultural heritage. Analyzing Arabic poetry is time-consuming and requires a high level of linguistic expertise; therefore, computational methods are useful, as they enable large-scale, extensive, and systematic analysis of poetry, thereby improving its accessibility for researchers and students. This article presents the first systematic review of natural language processing (NLP) and machine learning (ML) approaches for Arabic poetry. It addresses the question of which research tasks, methodologies, datasets, and evaluation approaches have been applied to Arabic poetry, and which trends and research gaps can be identified in the existing literature. In accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), the author conducted an exhaustive search across six major academic databases (ACL Anthology, IEEE Xplore, ACM, SpringerLink, Science Direct, and Google Scholar) for relevant studies published between January 2010 and May 2025. Eligibility was evaluated in several phases, and re-examination was conducted to ensure accuracy. The author performed task-level categorization, extracted key characteristics from each study, synthesized the findings, and presented them in tables and figures to highlight the main trends and research gaps in the literature. This study presents the first structured task-level synthesis of the field, identifying methodological trends, detecting evaluation inconsistencies, and highlighting research gaps that have not been critically consolidated before. Furthermore, the author assembled a comprehensive collection of available datasets and resources to promote standardized assessment.
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
