Bibliographic record
Abstract
Les trois termes les plus fréquemment utilisés et qui sont associés à l’interdisciplinarité – multidisciplinarité, interdisciplinarité, transdisciplinarité – constitutent la principale taxinomie pour comprendre les degrés d’interaction et d’intégration entre les disciplines ainsi que les degrés de collaboration au sein des équipes de recherche. Les typologies distinguent également les activités méthodologiques qui visent à améliorer les résultats théoriques à la faveur d’une meilleure compréhension des enjeux épistémologiques. La construction d’un « pont » laisse intactes les approches existantes, alors que la restructuration produit de l’interdisciplinarité et des champs nouveaux. La résolution instrumentale ou opportuniste des problèmes diffère des formes critiques qui interrogent la structure existante de la connaissance et de l’éducation. La transdisciplinarité inclut la quête d’unité de même qu’un nouvel encadrement en vue de comprendre les phénomènes complexes et la recherche trans-sectorielle qui implique les acteurs de la société.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".