Bibliographic record
Abstract
La neuroéconomie excite à la fois la fascination et la méfiance des économistes. La popularité qu’elle a acquise au cours des dernières années en incite plusieurs à se questionner sur la relation qu’elle est appelée à développer avec les approches mieux établies. Dans cet article, nous soutenons que la neuroéconomie est promise à un bel avenir puisqu’elle offre des outils essentiels pour expliquer la prise de la décision. Du même souffle, nous nous montrons cependant sceptiques quant à sa capacité à contribuer à l’étude de la plupart des phénomènes qui retiennent traditionnellement l’attention des économistes dont, au premier chef, les prix et les équilibres de marché. À partir de l’exemple des préférences sociales, nous donnons des raisons de penser que la neuroéconomie est peu susceptible de mieux contribuer à modéliser les fonctions d’utilité des agents économiques réels que ne le font déjà l’économie comportementale et la psychologie cognitive.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".