Charting Specific Paths: A Decade of English for Specific Purposes Research (2015-2024)
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.027 | 0.038 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".