Isolating the effect of rater experience as a time‐variant predictor of performance ratings
Bibliographic record
Abstract
Abstract A defining but sometimes overlooked characteristic of performance appraisals is that they are cyclical. The cyclical nature of performance appraisals makes it important to consider time‐variant definitions and operationalizations of constructs such as rater experience. In the current study, we work to clarify the association between rater experience and performance ratings by operationalizing rater experience as the number of appraisal cycles raters participated in. We did so while controlling for other similar but distinct operationalizations of experience such as span of control (number of ratees per rater) and familiarity with ratees. Furthermore, we employed a multilevel longitudinal design and analysis that allowed us to model rater experience as a time‐variant predictor of performance ratings and isolate its effects from both between‐rater and organizational context effects. The data were real appraisal data from a large South American company that contained 9233 ratees, across five appraisal cycles from 893 raters in 29 different business units, resulting in 24,608 observations. Our results revealed that rater experience had a small but statistically significant positive association with performance ratings. We also found that familiarity and span of control, were positively and negatively associated with performance ratings, respectively. Implications for practice and research are discussed.
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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 | 0.018 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".