Calibration Committees and Rating Distribution Guidance Effects on Leniency Bias in Subjective Performance Evaluations
Bibliographic record
Abstract
Firms use both calibration committees and rating distribution guidance to reduce leniency bias in subjective performance ratings. Leniency bias is the tendency to provide subordinates with higher ratings than deserved which can weaken the link between incentives and effort, leading to suboptimal and subordinate performance. I employ a 2x2 online experiment to assess how the presence versus absence of peer calibration committees [PCCs] and rating distribution guidance [RDG] affects leniency bias present in supervisors’ ratings of subordinates’ performance. I find support that supervisors may display more leniency in ratings prepared in anticipation of a PCC, especially among low performers. As the increased bias appears to impact low-performers, this may create additional fairness concerns for moderate and high-performers, which could demotivate these subordinates. Next, I find support that rating distribution guidance does have a main effect of reducing the leniency bias displayed among low and high performers. Further, using planned contrast testing, I find support for my predicted pattern of results for low performers. That is, the presence of a PCC has a main effect of increasing leniency bias, the presence of RDG has the main effect of reducing leniency bias, and the interactive effect such that when a PCC is present, the presence of RDG weakens the effect of PCCs on leniency bias. This finding indicates that rating distribution guidance may be helpful in settings with a PCC.
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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 | 0.013 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".