Successes, drivers and barriers of Education for Sustainable Development in Canada, England and Australia
Bibliographic record
Abstract
The world is plagued by a daunting array of interrelated environmental crises, economic instability and social injustice. In order to mitigate these issues for present and future generations we look towards the concept of sustainable development (SD) and hence education for sustainable development (ESD) which supports SD. In this qualitative study, participants from varying roles in Canada, England and Australia were interviewed to determine their perceptions and understanding of current practices, successes, drivers and barriers of ESD and changes over time. The progress of ESD in these three countries was compared and contrasted to discover that although they use different terms to describe the work, and have different structures and sociopolitical situations, there was much agreement about what has been accomplished and what needs to be done. Strong leadership, government policy, financial and staff support were seen as drivers, while lack of time and commitment were seen as barriers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".