Regional centres of expertise as mobilising mechanisms for education for sustainable development
Bibliographic record
Abstract
Chapter Eight presents three case studies of regional centres for expertise (RCEs) in ESD. RCEs were set up to achieve the aspirations of the UN Decade for Sustainable Development (DESD), 2005–14, and to help create a global learning space for sustainable development. An RCE is a network of formal, informal and non-formal organisations mobilised to act as a catalyst for the delivery of ESD. Although sharing common aims, RCEs have a considerable degree of autonomy and are able to determine their own particular priorities based on local circumstances.The three case studies are: RCE Saskatchewan, Canada; RCE Greater Sendai, Japan; and RCE Greater Nairobi, Kenya. These RCEs have all grown up organically and have been developed by a variety of social actors and stakeholders in their respective regions. They all have different focuses and have responded in different ways to the challenges of sustainability. This is a good example of subsidiarity in terms of ESD policymaking and practice. The case studies are framed within the context of civil society organisations and social movements, with an analysis of the impact and effectiveness of RCEs as agents for change.
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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 | 0.001 | 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".