Bibliographic record
Abstract
The effects of rating scale formats on several indices of the usefulness of performance appraisal for employee development were examined. The job performance of 96 police officers was rated using simple graphic scales or one of two behaviorally oriented rating formats: behaviorally anchored rating scales (BARS) and behavior observation scales (BOS). As predicted, ratees’ satisfaction with performance appraisal was highest and their perceptions of performance goals most favorable when using BOS. In addition, performance improvement goals for officers rated using BOS were judged by experts to be most observable and specific. Contrary to the authors’ predictions, graphic rating scales were generally as good as BOS and as good as or better than BARS when evaluated in terms of ratee attitudes and goal characteristics. The results suggest that different behaviorally-oriented rating formats can enhance or inhibit the developmental applications of performance appraisal. Researchers and experts in performance appraisal have suggested two broad uses of appraisal in organizations (McGregor, 1957; Wexley, 1979). First, it serves administrative purposes in areas such as reward allocation (salary increases, bonuses) and assignment decisions (promotions, transfers, demo-tions). Second, it contributes to employee development in that it makes This paper was based on data collected by Christine Joanis as part of her master’s thesis research conducted under the supervision of Aharon Tziner. Correspondence regarding this
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.428 | 0.138 |
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; the direct Gemma label and the distilled Codex classifier 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".