Partnerships between formal and non-formal educational settings: A way to implement a transformative and sociocritical education about environment and sustainable development
Bibliographic record
Abstract
Highlights: - Study on environmental education and sustainable development within the field of social science education. - Partnerships between formal and non-formal settings to support transformative and sociocritical education. - Qualitative study involving 23 participants from France and Canada (Québec). - Analysis reveals eleven conditions conducive to partnership. - Recommendations for key people who could be helpful in developing a partnership. Purpose: Current programs of study in France and Québec promote the integration of environmental education and sustainable development (EESD). This study aims to identify what conditions are suitable for establishing a partnership between settings in order to support a transformative and sociocritical EESD. Design/methodology/approach: This qualitative study involved a total of 23 participants from formal and non-formal settings. Each participant took part in a semi-structured interview. Four themes emerged from the thematic analysis: a) definition, b) benefits, c) terms, and d) obstacles and facilitators of partnership. Findings: The analysis identified eleven conditions conducive to successful partnership. Research limitations/implications: The participants included in the study showed a strong interest in issues related to EESD. Therefore, not all opinions are represented. Our research team is currently investigating the collaborative dynamics of specific partnerships between settings. Practical implications: This study highlights the role of key people in developing partnerships between formal and non-formal educational settings.
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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 | 0.026 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".