Engagement et incitations : comportements économiques sous serment
Bibliographic record
Abstract
Sous l’impulsion, notamment, de l’essor de l’économie expérimentale, la littérature récente a mis en évidence un large éventail de situations dans lesquelles les incitations monétaires échouent à orienter les comportements dans le sens désiré. Ce constat conduit à rechercher des mécanismes institutionnels alternatifs, capables de se substituer aux incitations monétaires. Cet article propose une revue des travaux s’inspirant de la psychologie sociale de l’engagement afin de développer des mécanismes non monétaires susceptibles d’affecter les comportements. Ces travaux étudient une procédure d’engagement particulière : un serment à dire la vérité. Cette procédure a été appliquée avec succès (1) au problème du biais hypothétique dans la révélation des préférences pour les biens non marchands, (2) aux défauts de coordination, et (3) à la propension à dire la vérité. Pris ensemble, ces travaux confirment la capacité de mécanismes d’engagement à guider l’élaboration d’institutions non monétaires capables d’orienter efficacement les comportements économiques.
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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 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".