Rating formats and rater training redux: A context-specific approach for enhancing the effectiveness of performance management.
Bibliographic record
Abstract
Dans cet article, nous procedons a l'examen critique des recherches anterieures sur les formats d'evaluation et l'entrainement des evaluateurs dans le contexte de la mesure des performances. Historiquement, le but de cette branche de recherche a ete de trouver des facons de maximiser les qualites psychometriques des donnees d'evaluation de la performance. Notre idee centrale est qu'il existe un certain nombre d'avenues pour elargir cette recherche. De fait, nous proposons un modele conceptuel qui pourrait servir de cadre conceptuel pour les travaux futurs portant sur ces deux questions de recherche traditionnelles en mesure de la performance. Par exemple, autant le format des evaluations que la formation des evaluateurs pourraient servir pour faciliter et ameliorer les retroactions et le processus de formation des employes ainsi que pour reduire les biais possibles dus a l'evaluateur. De plus, les recherches sur le format et l'entrainement pourraient permettre de trouver des facons de maximiser les reactions de l'evaluateur et de la personne evaluee au systeme de mesures. Une caracteristique centrale de notre modele est l'integration de la culture nationale comme variable moderatrice des relations entre des formats donnes, les programmes d'entrainement et differents resultats. Nous considerons autant la culture nationale de l'evaluateur que de la personne evaluee et nous portons une attention particuliere aux comparaisons entre les cultures asiatiques occidentale et orientale.
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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 | 0.095 | 0.284 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".