The Power of Deficit Discourses in Student Talk about Writing
Bibliographic record
Abstract
Does engagement with writing centre consultants in one-on-one consultations help students shift from remedial discourses toward meta-cognitive awareness more in keeping with the nature of peer review in an academic setting? This study investigates this question through looking longitudinally over a four-year period in a Canadian university writing centre. We situate this research within wider discussions of Standard English and remediation in student academic writing, as well as writing centre research that explores correlations between numbers of writing centre visits and both students’ confidence as writers and their intrinsic motivation. Using a corpus-supported genre and discourse analysis, we focus on student appointment requests, as well as summative writing centre consultant notes. Results suggest that deficit discourses are highly tenacious, which we explain in part as the result of the constraints inherent in the genre of requests for help, and also in terms of the institutional positioning of writing centres.
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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 | 0.018 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".