Writing Centers' Praxis Is Not Neutral but Raced
Bibliographic record
Abstract
This chapter begins by questioning the existing practices of writing centre tutoring. Based on the first author's writing centre tutoring experience and some artifacts, such as consultation notes, consultation forms, and feedback on student essays, the authors question whether the writing centre is truly a safe and neutral space for post-secondary writers and whether writing tutoring feedback contains some Eurocentric racial discourses that are complicit and coded in a way that sounds so called objective. Drawing on Lemke's principle of intertextuality, the authors highlight how standardized academic writing expectations have been unconsciously normalized and naturalized in writing centre tutoring discussions, thereby reinforcing the tutor's authority. In the end, we are in the position to look for an alternative, transformative change in the writing centre tutoring practice and a structural shift that can go beyond “remedial writing service provider.”
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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 | 0.006 | 0.011 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".