Une hybridation de l'économie et des neurosciences a-t-elle un sens ?
Bibliographic record
Abstract
La neuroéconomie est une extension récente de l’économie comportementale qui cherche à mettre en évidence les activités et les mécanismes cérébraux qui sous-tendent les comportements normaux ou déviants par rapport à la théorie économique de la rationalité. Gul et Pesendorfer [2005] ont formulé une critique sérieuse contre la validité épistémologique de la neuroéconomie. Une de leurs critiques fondamentales est basée sur l’incommensurabilité des concepts utilisés en économie et en neurosciences. Nous répondrons à cette critique par l’exemple, en envisageant différentes directions d’ajustement possibles entre les deux disciplines. Cette hybridation disciplinaire peut mettre en relief l’unité des sciences comportementales.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.030 |
| Scholarly communication | 0.016 | 0.029 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".