Bibliographic record
Abstract
As writing centres in Canada face challenges to their existence, funding, and stature, it may be helpful to situate the Canadian experience empirically. This project investigates the number of, geographical, institutional, and physical locations of, and longevity of Canadian writing centres using information from an original database and survey examining writing centres located outside the United States. In the study, findings from Canada are compared to those from the United States, where the only other comprehensive investigations of writing centres have taken place. Results demonstrate that 123 writing centres in Canada are located in all 10 Canadian provinces as well as the Yukon territory, almost half of centres operate under the academic affairs umbrella of their university and are physically located in the library, and that while writing centres in Canada are newer, on average, than their U.S. peers, they may be located in proportionally more universities. Unfortunately, the changes Canadian writing centres are experiencing are not new, as writing centres have previously faced challenges to their existence and place in the university. However, information about the number, institutional and physical location, and longevity of Canadian writing centres may be useful to administrators as they advocate for and further develop their writing centres.
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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.020 |
| Science and technology studies | 0.023 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".