Relationships Between Attitudes Toward Organizations and Performance Appraisal Systems and Rating Behavior
Bibliographic record
Abstract
Data collected in seven separate samples in three countries (the USA, Canada and Israel) were used to examine the relationships between perceptions of one’s organization (climate, commitment), beliefs about appraisal systems (self‐efficacy, uses of appraisal) and raters’ orientations to appraisal systems (confidence and comfort) and specific rating behaviors. We obtained good fits for structural models suggesting that attitudes and beliefs accounted for substantial variance in raters’ likelihood of giving high or low ratings, willingness to discriminate good from poor performers, and willingness to discriminate among various aspects of job performance when completing actual performance ratings. Proximal attitudes and beliefs (i.e., those directly related to the performance appraisal system) appear to have stronger links to rating behavior than do more distal attitudes (i.e., attitudes toward the organization in general).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".