Utilisation du Mini Entrevues Multiples en contexte francophone
Bibliographic record
Abstract
Le succès académique antérieur s’avère le meilleur prédicteur du succès académique en médecine, mais cet indicateur n’informe en rien quant aux habiletés non cognitives des candidats. Le Mini Entrevues Multiples (MEM), une mesure d’habiletés non cognitives, démontre une bonne fidélité et validité prédictive. L’objectif de la présente étude était de mesurer la fidélité du MEM UdeS élaboré et administré dans le cadre du processus de sélection au doctorat en médecine de l’Université de Sherbrooke. Les résultats observés démontrent la fidélité de l’outil, et ce, dans un contexte d’administration différent des études précédentes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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; both teacher heads agree on what is shown here.
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".