Threads, Woven Together: Negotiating the Complex Intersectionality of Writing Centres
Bibliographic record
Abstract
The Canadian college where the authors are employed has an ethos that supports its writing centre’s commitment to promoting equitable access to power, education, and employment. In recent years, one result of this ongoing commitment has been the hiring of tutoring staff with diverse identities and life-situations (in terms of race and ethnicity, dis/ability, and sexuality). The authors of this paper, one of them the director of the centre and the other a consultant at the centre, draw on their personal experiences and observations to discuss one unexpected consequence of the push for inclusivity: tutees sometimes struggle to process the demand for social literacy and cross-cultural competence placed on them during encounters with tutors who have non-mainstream identities or affiliations. Seeking to understand the pedagogic and ethical complexities of encounters specifically between tutees and racialized tutors, we propose that responsibility for effecting positive social change—for building a “brave space”—through patient relationship-building and commitment to critical consciousness be allocated to the writer and the tutor, but above all to the writing centre as a collective.
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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 | 0.025 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.032 | 0.058 |
| Scholarly communication | 0.036 | 0.030 |
| Open science | 0.004 | 0.040 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".