<scp>CEO</scp> Power and Relative Performance Evaluation
Bibliographic record
Abstract
Abstract We model relative performance evaluation (RPE) when a Chief Executive Officer (CEO) has the power to opportunistically influence the design of RPE by choosing the weight on an index‐based peer group or by customizing the selection of peers comprising a peer group. A powerful CEO compares the benefits of reducing common risk affecting his compensation with the benefits of receiving a higher bonus by economizing on expected peer‐group performance. As a consequence, the Board of Directors (BoD) is less likely to use RPE. Our analytical model yields hypotheses predicting that powerful CEOs choose to reduce common risk only partially and that BoDs choose to not implement RPE if expected peer performance is sufficiently high. Our model has further empirical implications in (i) providing new interpretations of tests for detecting strong‐form and weak‐form RPE in the presence of powerful CEOs, and (ii) suggesting a new empirical measure of CEO power with a focus on the delegation of RPE decision rights.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".