Targeting Language Support for Non-Native English-Speaking Graduate Students at a Canadian University
Bibliographic record
Abstract
Universities and colleges in Canada and other English-speaking countries have become increasingly concerned with linguistic and cultural diversity and internationalizing their campuses, both to enhance local and international students' experiences on campus and to prepare them to function in their careers and the larger society. Most international students are non-native English-speaking (NNES) and need support to develop the English language proficiency required for engagement in the academic demands of the Canadian university milieu. This small-scale study at a Canadian university, by way of a survey and follow-up interview, addresses the gap in our understanding between academic skills that are required at the graduate level and those that learners of English find difficult. The findings suggest that by targeting academic skills that are both required and difficult, efficiency can be achieved in the design of programmatic supports for developing English for academic purposes (EAP). The findings further suggest that international students may lack independent strategies for advancing their English-language proficiency and that these too can be targeted in an EAP program.
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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 | 0.009 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".