Transdisciplinarité et écologisation d’une formation universitaire : une pratique critique à partir du paradigme de la complexité
Bibliographic record
Abstract
Cet article expose des perspectives d’écologisation de la formation par la mise en œuvre d’un modèle critique fondé sur l’épistémologie transdisciplinaire de la complexité. Selon ce modèle, l’approche transdisciplinaire écologise les savoirs c’est-à-dire qu’elle les met en dialogue avec ceux de l’environnement en même temps qu’elle introduit l’environnement comme préoccupation majeure des connaissances et des apprentissages. Cette dynamique est illustrée à partir des résultats provisoires d’un programme de recherche-action-formation visant l’expérimentation d’un modèle transdisciplinaire au Centre d’études universitaires Arkos de Puerto Vallarta au Mexique. Les premiers résultats montrent que les mémoires des étudiants de licenciatura en droit, comptabilité, commerce international, communication et administration du tourisme sortent du cadre réduit des problèmes techniques disciplinaires pour aborder des problématiques sociales et environnementales complexes.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.037 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".