Working With Social Comparisons in the Appraisal and Management of Performance
Bibliographic record
Abstract
Research and practice in performance appraisal and performance management seem to suffer from the same “delusion of absolute performance” that Rosenzweig (2007, p. 112) described with respect to commentators’ evaluations of company performance in a competitive market economy. Commentators on business success factors have tended to speciously neglect or downplay the relative nature of performance (Rosenzweig, 2007). Downplaying the relative nature of performance is apparently the strategy endorsed by most performance appraisal scholars, too. Goffin, Jelley, Powell, and Johnston (2009) estimated that less than 4% of the published performance rating research has involved relative or social-comparative approaches, despite demonstrable advantages for relative over absolute rating formats (discussed below). Similarly, social comparison research and organizational scholarship have not traditionally been closely integrated (Buunk & Gibbons, 2007; Greenberg, Ashton-James, & Ashkanasy, 2007).
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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 | 0.199 | 0.288 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.010 | 0.065 |
| Scholarly communication | 0.023 | 0.041 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".