Bibliographic record
Abstract
ABSTRACT This paper studies whether dissemination of private, pre-decision signals about productivity is valuable to the principal when agents work sequentially and observe each other's effort. The benefit of dissemination is that when productivity states are correlated, each agent's signal is useful as a performance measure for the other agent and for making efficient production choices. The more informative the signal is about agents' efforts, the greater the benefit of the additional performance measure, but there is a cost due to higher information rents. With no dissemination, the downstream agent learns about the upstream agent's productivity state by observing that agent's effort. The upstream agent's rents are lower because he has no incentive to free-ride on the downstream agent, who follows his effort, but there is less information on which to base the payments. The choice between dissemination and no dissemination depends on the informativeness of the signal about agents' efforts. JEL Classifications: D82; D83; J41; M41.
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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 | 0.009 | 0.003 |
| 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 | 0.001 |
| 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 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".