La création de l’École de médecine du Nord de l’Ontario
Bibliographic record
Abstract
Twelve contributors highlight the various aspects of the school's development and the unique opportunities it offers. The first new medical school in Canada in over thirty years, the Northern Ontario School of Medicine provides a blueprint for those interested in an innovative approach to medical education. This collection provides a fascinating and detailed account of the challenges and rewards faced by those who insisted on creating a patient-centered, community-based, and culturally sensitive learning environment for the physicians of tomorrow. Contributors include Arnie Aberman (University of Toronto), Hoi Cheu (Laurentian University), Geoffrey Hudson (Northern Ontario School of Medicine), Dan Hunt (Liaison Committee on Medical Education, Washington, DC.), Jill Konkin (University of Alberta), Joel Lanphear (Northern Ontario School of Medicine), John Mulloy (MD, Northern Ontario), Raymond Pong (Laurentian University), Roger Strasser (Northern Ontario School of Medicine), Geoffrey Tesson (Laurentian University), John Whitfield (Lakehead University), and Dorothy Wright (Northern Ontario School of Medicine).
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".