Bibliographic record
Abstract
Many of the celebrated and generative tropes that defined the work and self-image of American writing centres—tropes like Stephen North’s “fix-it shop in the basement,” Andrea Lunsford’s “Burkean Parlour,”and Kenneth Bruffee’s “conversation of mankind”—also helped create and affirm an apparent scholarly and pedagogic consensus about writing centre praxis in the Canadian context. I examine the way such tropes imagine our practices—dialogical guidance, collaborative learning, scaffolding, and relationship-building—and the bodies and minds that are enacting them. Using sonnets, narrative, and reflection to propose alternative tropes, I explore how the entry of othered bodies and minds, new perspectives, and marginalized cultures into the writing centre world might change the way we relate to each other and the way we re-imagine our collectivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.074 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".