cial Management Association Meeting (Toronto), Southern Finance Association Meeting (Destin), and the Harvard Finance Lunch Seminar for their comments. I would like to
Bibliographic record
Abstract
This paper analyzes the link between equity-based compensa-tion and created incentives by (1) deriving a measure of incentives suitable for both linear and non-linear compensation contracts, (2) analyzing the effect of risk on incentives, and (3) clarifying the role of the agent’s private trading decisions in incentive creation. With option-based compensation contracts, the average pay-for-performance sensitivity is not an adequate measure of ex-ante in-centives. Pay-for-performance covaries negatively with marginal utility and hence overstates the created incentives. Second, more noise in the performance measure implies that the manager is less certain about the effect of effort on performance, which in turn makes her less willing to exert effort. Finally, the private trading decisions by the manager have first-order effects on incentives. By reducing her holdings of the market asset, the manager achieves an effect similar to ”indexing ” the stock or option grant, making explicit indexation of the contract redundant.
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.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.190 | 0.039 |
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".