Is there a Preferred Performance Rating Format? A Non‐psychometric Perspective
Bibliographic record
Abstract
Les échelles d’appréciation constituent une forme d’évaluation des performances qui a rencontré un grand succès ces dernières décennies. En particulier, de gros efforts ont été consacrés au développement d’échelles relativement insensibles aux erreurs et biais cognitifs. Mais on s’est beaucoup moins intéressé au fait de savoir si et comment le type d’évaluation affecte les attitudes professionnelles et les réactions des personnes évaluées. Des données en provenance de quatre études différentes avec des échantillons tirés dans deux pays (lsraël et le Canada) apportent des éléments en faveur de l’idée selon laquelle une évaluation de la performance basée sur le BOS peut être supérieure aux autres méthodes en ce sens qu’elle entraîne des conséquences plus positives au niveau des attitudes. One aspect of performance appraisal that has received considerable attention over past decades is the rating format. In particular, much effort has been devoted to developing rating scales that are relatively impervious to cognitive rating errors and biases. However, much less attention has been accorded the issues of whether and how an appraisal’s format affects work attitudes and reactions of ratees. Data collected in four separate studies and with samples in two nations (Israel, Canada) lend credence to the proposition that a BOS‐based performance appraisal and review may be superior to other appraisal methods in terms of yielding more favorable attitudinal effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.250 | 0.389 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".