Regional centres of expertise as mobilising mechanisms for education for sustainable development
Bibliographic record
Abstract
Introduction This chapter will examine the role of regional centres of expertise (RCEs) in promoting and delivering change towards sustainability through education and learning. It will take a number of case studies from different global regions and identify some key challenges and opportunities. The central core text for these case studies is provided by one of the very first regions to adopt this RCE model, that of RCE Saskatchewan in Canada, which was started in 2005. Lyle Benko and Roger Petry from RCE Saskatchewan have highlighted a number of key elements and issues for their RCE and have identified some key enabling factors that have led to examples of effective mobilisation. Although every RCE is different in the challenges that they face, as well as in their development and structure, nonetheless, some interesting comparisons can be made with the other two case studies of RCE Greater Sendai in Japan (contributed by Takaaki Koganezawa and Tomonori Ichinose) and RCE Greater Nairobi in Kenya (contributed by Mary Otieno). Each RCE has grown up organically, developed by the various concerned social actors in their regions. They all have different focuses and have responded in different ways to the challenges of their regions. This is an example of a kind of subsidiarity in terms of policymaking and practice in education for sustainable development1 (ESD) and will be considered in relation to their effectiveness as mobilising mechanisms for ESD. Acccording to Professor Hans Van Ginkel, one of the founders of the RCE initiative while Rector of the United Nations University (UNU): ‘Education for Sustainable Development’ means what it says: it is not just environmental education or even sustainable development education, but ‘education for sustainable development’. Only when we are successful in pooling all available people and resources, can we do an appropriate job. We must ‘walk the talk’ in order to transform all education and transcend all existing divisions to achieve our ultimate goal of a better future for all. (Van Ginkel, 2013, p 92) What is a regional centre of expertise? RCEs were set up to achieve the aspirations of the United Nation's (UN’s) Decade of Education for Sustainable Development (DESD), 2005–14. The UNU Institute of Advanced Studies (UNU-IAS) intends that the network of RCEs around the planet will become part of a global learning space for sustainable development.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".