Bibliographic record
Abstract
EAP has changed over the last 30 years as new insights into academic discourse have informed the content ofcourses across the Anglophone world. However, this paper will argue that the biggest drivers of change in comingdecades will be changes to education globally that will see new generations of students commencing their EAPstudies from a higher initial level of English competence, and also studying in different locations and in differentinstitutions. Globalisation is causing an increasing number of countries to rethink their English language educationpolicies, and this will change the profile of students exiting secondary education in those countries. This combinedwith the emergence of a variety of new institutions offering higher education in English will reshape the global EAPlandscapes and provide new challenges and opportunities for the EAP profession. This paper explores these changesand predicts how they will shape EAP in the future.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.015 | 0.025 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.067 | 0.016 |
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".