Bibliographic record
Abstract
We consider a delegation problem with a biased and potentially uninformed agent when the principal cannot use monetary payments. If the bias between the principal and the agent is large then the optimal delegation set is an interval. When the bias is small or medium the optimal delegation set is no longer connected. It can be one of two types: 1) with an interval and low option, 2) with two intervals. In all cases the agent has less discretion. However, in the case of medium bias the principal delegates in a wider range than in the case of an informed agent. / Nous considérons un problème de délégation avec un agent potentiellement mal informé lorsque le principal ne peut pas utiliser les paiements monétaires. Si l'écart entre le principal et l’agent est grande l'ensemble optimal de délégation est un intervalle. Lorsque le biais est petit ou moyenne l'ensemble optimal de délégation n'est plus connecté. Il peut s'agir de deux types: 1) avec un intervalle et l'option faible, 2) avec deux intervalles. Dans tous les cas, l'agent à moins de discrétion. Toutefois, dans le cas de milieu biaiser les délégués principaux dans une gamme plus large que dans le cas d'un agent informé.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; both teacher heads agree on what is shown here.
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".