MétaCan
Menu
Back to cohort
Record W4416192754 · doi:10.5430/wjel.v16n2p358

Charting Specific Paths: A Decade of English for Specific Purposes Research (2015-2024)

2025· article· W4416192754 on OpenAlexvenueno aff
Lim Seong Pek, Rita Wong Mee Mee, Fatin Syamilah Che Yob, Khairul Firdaus Ne'matullah, Ali Derahvasht, Henry E. Lemana

Bibliographic record

VenueWorld Journal of English Language · 2025
Typearticle
Language
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
FundersNational Defence University of Malaysia
KeywordsThematic analysisScholarshipVocabularyCurriculumWorkforceHigher educationInstitutionalisation

Abstract

fetched live from OpenAlex

Research on English for Specific Purposes (ESP) has grown rapidly over the last decade, yet systematic mapping of its intellectual structure, thematic patterns, and global contributions remains limited. Despite the proliferation of studies on curriculum design, needs analysis, genre pedagogy, and technology-enhanced learning, there is still no consolidated overview that captures how ESP scholarship has evolved and where it is heading. To address this gap, this study applies bibliometric analysis to 381 publications on ESP indexed in the Web of Science Core Collection between 2015 and 2024. Performance analysis was employed to identify the most influential authors, journals, institutions, and countries; co-citation mapping was conducted to uncover foundational works and theoretical orientations; and keyword co-occurrence analysis was used to reveal emerging themes and research priorities. The findings indicate that five co-citation clusters – genre-based theory, curriculum design, data-driven instruction, English Medium Instruction (EMI) integration, and learner motivation – constitute the intellectual foundations of ESP. Meanwhile, co-occurrence analysis identifies five thematic clusters centered on learner-centered pedagogy, corpus-informed vocabulary learning, business and higher education contexts, digital integration, and needs-based instructional design. These results highlight the field’s transition from traditional linguistic concerns toward affective learner dimensions, technology-mediated learning, and transdisciplinary applications. By systematically mapping the ESP research ecosystem, this study contributes an evidence-based overview that benefits scholars, educators, and policymakers. It emphasizes ESP’s increasing institutionalization in higher education, its role in workforce preparation, and its alignment with Sustainable Development Goal 4 (Quality Education), thereby offering a roadmap for future research and practice.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.973
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0270.038
Science and technology studies0.0020.002
Scholarly communication0.0110.012
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.041
GPT teacher head0.320
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueWorld Journal of English LanguageSame topicSecond Language Learning and TeachingFrench-language works237,207