QU’EST-CE EXACTEMENT QUE L’INTERDISCIPLINARITÉ? QUELQUES ÉLÉMENTS DE RÉPONSE
Bibliographic record
Abstract
Cet article envisage l’interdisciplinarité à la lumière de certaines définitions accolées à ce mot en soulignant les problèmes que posent ces acceptions devenues courantes. L’interdisciplinarité, par exemple, met en cause la spécialisation des savoirs issues de la science. Or, à la lumière de l’épistémologie contemporaine, la science, conçue comme connaissance par objet et par concept, requiert d’entrée de jeu le découpage de la « réalité », lequel génère des connaissances spécifiques pour en rendre compte. La combinaison des connaissances produites par chaque discipline trouve son droit et son fait quand il s’agit d’intervenir pour remédier à une situation ou résoudre des problèmes sociaux en termes pratiques. L’article aborde en conclusion les enjeux politiques que soulève l’interdisciplinarité dans l’enseignement et la recherche universitaires et face au pouvoirs publics.
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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 | 0.021 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.043 |
| Scholarly communication | 0.022 | 0.027 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".