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).
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 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 | 0.000 |
| 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".