Bibliographic record
Abstract
Welcome to our final issue of 2024 and the completion of our 15 th year of operation!We have much to celebrate.We had great yet unfulfilled plans for a 10-year celebration at the Canadian Conference on Medical Education in April 2020, and we all know what happened then.However, in 2025, at the International Congress on Academic Medicine (https://icam-cimu.ca/#eventdetails-program),you will see a lot of the CMEJ.Again, this year, we present the "Top Articles from the CMEJ."It was very successful last year, the first time we ran that event. 1 In addition, we will have a highly visible display where you can meet some of our editors and learn more about the CMEJ.When picking up your registration package, you may also snag a physical handout with some interesting facts about the CMEJ, and the abstracts of the articles highlighted at our "Top Articles" session (so that you may read up ahead of time).Welcome also to the next five years of the CMEJ's contributions to medical education here in Canada and internationally!With 15 years of history as a player in medical education, we are looking ahead to the next five and how we might continue to lead and bring greater insight and innovation to our readers.
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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 | 0.012 | 0.063 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.024 | 0.023 |
| Insufficient payload (model declined to judge) | 0.070 | 0.043 |
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".