Private Information, Performance Measurement Bias, and Leading by Example
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT This paper studies the effect of performance measurement error and bias on the principal's preference for a leader, who signals private information about a favorable common shock to a follower. Without a leader, both agents are privately informed and relative performance evaluation is optimal due to its ability to remove the common shock. An increase in the conservative bias can increase or decrease compensation, depending on the likelihood of the common shock. With leading by example, joint performance evaluation can be optimal for the leader, reducing the leader's incentives to free ride on the follower and an increase in the conservative bias reduces compensation. The principal prefers a leader if the likelihood of the common shock is low, or if agents' outputs are more likely to be independent. Further, the more accurate the performance measure, the principal's preference for a leader decreases, but the effect of conservatism is mixed. JEL Classifications: D23; D82; J33; M41.
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.
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 | 0.008 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.007 |
| Open science | 0.001 | 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 it