Comments on the 2018 Ottawa Consensus Statement - An Admissions Officer’s Point of View
Bibliographic record
Abstract
This article was migrated. The article was not marked as recommended. Patterson et al. (2018), after a multi-stage consultation of scholars in the health professions and education communities, presented an updated Ottawa consensus statement, in which they sketch out four critical issues and ten recommendations to advance the field of recruitment and selection. There is no question that this work cannot be achieved in isolation. A symbiotic relationship among health education scholars, admissions committees and admissions officers will bolster this knowledge mobilization process. I argue that admissions officers’ role is to facilitate this collaboration and modernization of selection criteria and methods using their tacit knowledge, experience, and networks.
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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 | 0.058 | 0.298 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.034 | 0.049 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".