Compensation Policy and Worker Performance: Identifying Incentive Effects from Field Experiments
Bibliographic record
Abstract
L'utilité des expériences sur le terrain afin d'évaluer l'effet de différents systèmes de compensation sur la productivité des travailleurs est investiguée. Une attention particulière est portée à la capacité des expériences d'identifier l'effet d'un changement permanent de la politique de l'entreprise. Bien que les expériences résolvent le problème d'endogénéité en permettant aux sujets d'être alloués à un système de compensation par un processus aléatoire, ceci est accompli dans un environnement spécifique et, à la fois, artificiel, qui ne saurait être répliqué par un changement permanent. Comme tel, plutôt que d'identifier l'effet incitatif sans restriction, les expériences nous fournissent la variation exogène qui permet d'identifier des paramètres structurels. Ces paramètres nous permettent d'évaluer les effets des changements de politiques de l'entreprise.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".