Bibliographic record
Abstract
L'influence de la richesse relative sur les préoccupations d'équité est analysée dans un jeu de l'ultimatum dans lequel les participants reçoivent d'importantes dotations initiales largement inégales. Au départ, les sujets démontrent un soucis d'équité. Cependant, avec le temps, leur comportement s'éloigne de la perfection en sous-jeux ainsi que de l'équité. L'estimation d'un modèle structurel d'apprentissage par renforcement montre des signes d'apprentissage autant chez les sujets qui proposent que chez les receveurs. Les résultats de l'estimation suggèrent que, lorsque guidés par les meilleures réponses possibles et par un sens acquis de ce qui leur est dû, les sujets riches deviennent plus égoïstes, alors que les sujets pauvres, influencés uniquement par leur expérience personnelle, apprennent à tolérer ce comportement.
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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".