Bibliographic record
Abstract
When university/college faculty members believe that ESL students’ writing skills are not equivalent to those of native speakers, they frequently send these ESL students to their institution’s writing centres (WCs). However, this often results in frustra- tion for WC staff, the students, and faculty members. This article first describes ESL students’ language-learning backgrounds and expectations, as well as WCs’ still- evolving philosophy and practices, to demonstrate that ESL students visiting WCs are still often caught between two opposing educational frameworks. The article then offers possible solutions and discusses the importance of initiating dialogue among ESL instructors, ESL students, WC staff, and university/college professors.Quand les professeurs au collège ou à l’université croient que la compétence en rédaction des étudiants en ALS n’est pas équivalente à celle des locuteurs natifs, ils les envoient souvent aux centres de rédaction de leur institution. Il en résulte souvent de la frustration de la part du personnel des centres, des étudiants et des professeurs. Cet article débute par une description des antécédents et des attentes des étudiants en ALS d’une part, et des pratiques et de la philosophie en évolution des centres de rédaction d’autre part, de sorte à démontrer que les étudiants qui vont aux centres de rédaction se retrouvent encore souvent coincés entre deux cadres pédagogiques en opposition. Cet article offre des solutions possibles et dis- cute l’importance d’amorcer un dialogue entre les enseignants en ALS, les étu- diants en ALS, le personnel des centres de rédaction et les professeurs aux niveaux collégial et universitaire.
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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 | 0.013 |
| 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 | 0.009 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".