Bibliographic record
Abstract
[First Paragraph] In Spring 2011 I was awarded a Fulbright Specialist Grant to "consult, collaborate, and inform" on the future of the Department of Rhetoric, Writing, and Communications at the University of Winnipeg, located in the city of Winnipeg in Manitoba, Canada. The Department of Rhetoric, Writing, and Communications department (hereafter, RWC) was a pioneer in writing instruction in Canada, where it became the first unit to establish itself independently as a department with a full-‐time faculty committed to both teaching and scholarship in writing and rhetoric. It remains a rare phenomenon on the Canadian higher education scene, where studies and programs in rhetoric and writing have developed late and along a different trajectory from the discipline in the United States. Because of its unique history, the faculty is positioned to make significant contributions to the further development of writing instruction and scholarship in rhetoric and writing in Canada and beyond. At the same time, the close fit between the department’s character and the mission of the institution makes it a strategic asset to the university. This report presents my findings, analyses, and recommendations to the Department and the Fulbright Specialist Program, based on six weeks of inquiry and conversations.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.017 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".