Bibliographic record
Abstract
Principal-agent problems are pervasive in economic settings. CEOs and shareholders, lawyers and clients, manufacturers and retailers, lenders and borrowers are all examples of settings in which moral hazard problems might arise. Incentive contracts in both individual and team environments have been studied by economists (see Shavell, 1979, and Holmstrom, 1982, 1979, for seminal theoretical work; and, Prendergast, 1999, for a survey of empirical literature). Contracts that tie an agent's compensation to performance, such as conditional bonus schemes, have been proposed as a way to align the interests of agents and principals. Experimental literature from economics and social psychology suggests that the way choices are framed can affect decisions as well. Hence, contract frames might influence the effectiveness of incentive schemes. This comment first outlines seminal experimental studies on frames and describes recent work that relates the incentive contract literature with the experimental work on frames. Second, it discusses the experimental design and findings of Brooks, Stremitzer, and Tontrup's (2011) work on individual incentives and contract frames.
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 | 0.018 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.038 | 0.029 |
| Insufficient payload (model declined to judge) | 0.020 | 0.010 |
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".