L’émerveillement comme levier pédagogique : Apports des scientifiques-écrivaines à l’éducation relative à l’environnement
Bibliographic record
Abstract
Cet article explore le potentiel pédagogique et philosophique de textes écrits par des scientifiques-écrivaines telles que Lynn Margulis, Suzanne Simard et Robin Wall Kimmerer pour susciter l’émerveillement (awe) et l’engagement écocitoyen. En révélant l’agentivité méconnue d’êtres vivants comme les bactéries, les champignons ou les arbres, ces textes peuvent provoquer un questionnement philosophique sur notre rapport au vivant. L’émerveillement qu’ils suscitent peut nourrir une pensée attentive (caring thinking) envers le monde vivant et un désir d’agir en sa faveur. Intégrés dans une démarche d’éducation relative à l’environnement, croisant approches littéraires, philosophiques et scientifiques, ces textes offrent des ressources fécondes pour développer une écologie de l’attention et de la réciprocité. Cet article esquisse des pistes pour une éducation à l’écocitoyenneté par l’éveil de la sensibilité et de la réflexivité.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".