La dissémination de la recherche en sciences économiques : les « cahiers de recherche »
Bibliographic record
Abstract
Publier en sciences économiques impose des délais considérables se chiffrant facilement en de multiples années, de la soumission à la parution. Aussi, le contenu des revues est en retard par rapport à la frontière de la recherche. Les principaux médias pour s’informer de cette frontière sont alors les conférences et les cahiers de recherche, des polycopiés qui circulent parmi certains scientifiques. Ceci favorise la formation de petits cercles fermés et exclut la participation de tiers à la pointe de la recherche. L’apparition d’Internet a fondamentalement changé l’accès aux cahiers de recherche, mais encore faut-il que cet accès soit organisé et qu’il permette d’être lu par les autres. Le présent article décrit RePEc, une initiative qui a justement permis d’accorder tous les économistes sur un dénominateur commun et qui est maintenant devenue un instrument incontournable dans leur domaine de recherche. Des initiatives similaires dans d’autres champs de recherche sont aussi abordées.
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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 | 0.144 | 0.273 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.046 | 0.027 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.020 | 0.026 |
| Insufficient payload (model declined to judge) | 0.044 | 0.061 |
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".