Bibliographic record
Abstract
We used an expectancy theory framework to predict rating inflation. Managers (N=106) provided confidential ratings of subordinates as well as measures of rating goals, valences and instrumentalities. Rating inflation (operationalized in terms of differences between confidential ratings and public ratings obtained from personnel files) varied as a function of ratee performance levels, with higher levels of inflation when confidential ratings indicated that the ratee was a poor performer. Cleveland and Murphy (1992) suggested that rating inflation is not an error, but rather is often an adaptive response on the part of the rater, who is likely to experience positive consequences if he or she gives high ratings (e.g., subordinates will be more satisfied, more motivated) and negative ones if he or she gives lower ratings (e.g., conflict with ratees, discomfort in giving negative feedback). Several authors have suggested that motivational factors might be important for understanding rater behavior (DeCotiis & Petit, 1978; Harris, 1994; Longenecker et al., 1987; Mohrman & Lawler, 1983; Murphy & Cleveland, 1995). In particular, Murphy and
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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".