At the United States Military Academy at West Point of the 1950s, plebes guilty ofminor
Bibliographic record
Abstract
This paper proposes that workers' self-image as jobholders, coupled with their ideal as to how their job should be done, can be amajor work incentive. It shows how such identities can flatten reward schedules, as they solve the "principal-agent" problem. The paper also identifies and explores a new tradeoff: supervisors may provide information to principals, but create rifts within the workforce and reduce employees' intrinsic work incentives. We motivate the theory with examples from the classic sociology of military and civilian organizations. * Akerlof: University of California, Berkeley; Kranton: University of Maryland. We especially thank Robert Akerlof for editorial comments and modeling suggestions. We also thank Abdeslam Maghraoui, David Sega!, and Janet Yellen for help and comments. Tomas Rau provided invaluable research assistance. George Akerlof is grateful to the Canadian Institute for Advanced Research for financial support. Rachel Krantonthanks the Institute for Advanced Study, where she was as a Deutsche Bank Member of the School of Social Science, for its hospitality and financial support, and the International Economics Section of Princeton University for its hospitality. I.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.044 | 0.008 |
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".