La relation entre le contexte de l'évaluation du rendement et l'indulgence de l'évaluateur
Bibliographic record
Abstract
La présente étude s'insère dans les multiples efforts de recherche déployés pour mieux circonscrire les paramètres de l'évaluation du rendement. À partir du modèle de Murphy et Cleveland (1995), les auteurs développent une méthodologie originale qui permet de tester empiriquement auprès de 106 fonctionnaires de la fonction publique québécoise la motivation de l'évaluateur à produire des évaluations indulgentes de leurs subordonnés. Les résultats révèlent que l'indulgence s'avère une réponse à un contexte défavorable d'évaluation : les variables contextuelles influencent significativement les appréciations faites par l'évaluateur.
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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 | 0.017 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".