bioéconomie en France. Une étude scientométrique
Bibliographic record
Abstract
Dans le cadre d’une recherche sur l’application des principes de la science ouverte au domaine de la bioéconomie, nous avons mené une étude scientométrique de la production scientifique de la France dans ce domaine, pour la période de 2015 à 2019. L’étude a permis d’identifier 1913 publications dans la base de données Scopus. Nous avons analysé ce corpus sous différents aspects : types et sources des documents, avec volumétrie et impact ; auteurs, organismes et établissements ; sources de financement ; degré d’internationalité et taux d’ouverture (libre accès). La discussion porte sur la terminologie et les sources d’une telle étude scientométrique, sur l’accessibilité des publications et sur la position de la France dans ce domaine. La conclusion propose quelques recommandations pour la conduite d’une étude similaire, notamment à destination des professionnels de l’information.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.025 | 0.049 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".