Strategy Use by Nonnative English‐Speaking Students in an MBA Program: Not Business as Usual!
Bibliographic record
Abstract
Despite the long‐standing interest in strategy use and language learning, little attention has been given to how social context may constrain or facilitate this use or the development of new strategies. Drawing on data from a longitudinal qualitative study, we discuss this issue in relation to the experiences of Chinese students from the People's Republic of China, who, following study in English for Academic Purposes courses, registered in a Masters in Business Administration program in a Canadian university. Specifically, we focus on how the contact with the native‐English‐speaking Canadian students mediated the Chinese students' strategy use in 3 domains: reading, class lectures, and team work. In contrast to the rather simplistic notion evoked in certain portrayals of the good language learner, strategy use as reported herein emerges as a complex, socially situated phenomenon, bound up with issues related to personal identity (Leki, 2001; Norton, 1997, 2000; Spack, 1997).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".