L’interdisciplinarité en pratique : retour d’expérience de la deuxième école d’été australe sur la vulnérabilité du patrimoine récifal (EEA VulPaRe 2016)
Bibliographic record
Abstract
En novembre 2016, s’est tenue à Toliara, dans le sud-ouest de Madagascar, la deuxième école d’été australe sur la vulnérabilité du patrimoine récifal (EEA VulPaRe). Coorganisée par l’IRD (Institut de recherche pour le développement, France) et l’IHSM (Institut halieutique et des sciences marines de l’Université de Toliara, Madagascar), cette formation a proposé une approche interdisciplinaire de la thématique des récifs coralliens. Elle a été dispensée sous la forme de cours magistraux, de pratiques de terrain et de débats portant sur les questions environnementales, de connaissance, de valorisation et de conservation de ces milieux. Les participants de l’EEA VulPaRe 2016 souhaitent, à travers le présent article, donner à la communauté scientifique un retour d’expérience critique sur une démarche originale de formation à la recherche interdisciplinaire.
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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 | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".