Bibliographic record
Abstract
English for Academic Purposes (EAP) and English for Specific Purposes (ESP) are language training pathways for students to meet target language proficiency requirements towards further tertiary studies (Keefe & Shi, 2017; Walková & Bradford, 2022). Yet, these can be highly debatable terms, controversial over their appropriate usage (Flowerdew, 2016; Li, 2020; Maleki, 2008; Mpofu & Maphalala, 2021; Wette, 2018). A main reason for this discord is a lack of firm definitions between these two constructs, which leads to vague and blurred boundaries. This article will describe the process where a small western Canadian university modified an existing EAP course consisting of broad reading and writing topics into an ESP one with a narrow culturally and medically themed focus towards internationally educated nurses (IENs). These modifications assisted in the identification of the similarities and differences of both EAP and ESP for the institution. The new ESP course, piloted over two terms was found to have strengthened student outcomes in their non-native target language, commonly known as L2 (Saville-Toike, 2012) while their disciplinary knowledge contributions also enriched the curriculum. Rooted within the debate between determining appropriate language pathways, recommendations to determine the suitability of an EAP versus an ESP course within a “negotiated syllabus” utilizing learner input are offered (Breen & Littlejohn, 2000; Prior, 2020).
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.031 | 0.010 |
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".