Évaluer l’efficacité de l’éducation relative au changement climatique en milieu non formel : une étude de cas
Bibliographic record
Abstract
Les outils permettant d’évaluer l’efficacité de l’éducation relative au changement climatique en milieu non formel sont peu nombreux et souffrent souvent de l’absence d’assises théoriques solides. Dans le contexte de la campagne Sors de ta bulle menée par la Fondation Monique-Fitz-Back, un cadre d’évaluation novateur reposant sur douze indicateurs a été élaboré. Ces indicateurs ont servi à évaluer les effets de la participation de jeunes de 12 à 17 ans au Sommet jeunesse sur le changement climatique, l’une des activités de la campagne. Un devis mixte combinant la réalisation d’une enquête en ligne auprès des participant.e.s et un entretien de groupe avec douze d’entre eux a permis de constater que la campagne Sors de ta bulle a contribué à stimuler efficacement le processus d’adoption de comportements écoresponsables selon trois axes : augmentation de la motivation, renforcement du sentiment d’efficacité personnelle et intensification de l’engagement dans la lutte climatique.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".