Newsletter Spring 2019: Clinician Investigator Trainee Association of Canada (CITAC)
Bibliographic record
Abstract
Message from the President: Demystifying and promoting the MD-PhD/MD+ world Since its inception in 2006, the Clinician-Investigator Trainee Association of Canada (CITAC) has investigated, supported and promoted the needs of Canadian trainees on track to building a career in research and medicine. Membership in CITAC signals interest in such a career as a physician/clinician/surgeon-scientist. MD+ trainees (e.g., those in Clinician Investigator Programs (CIP), MD-PhD and MD-MSc programs) are involved in discussions surrounding this dual career often enough to have a sense of what it entails. Trainees assume and trust that the training they receive, and the opportunities they seize, can lead them to the dual career. Yet, when considering practical questions about this career outcome, such as what is the success rate in landing a faculty position with that job description, we realize that much is unknown.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.017 | 0.008 |
| Insufficient payload (model declined to judge) | 0.230 | 0.136 |
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".