Building Regional Capacity for Sustainable Development through an ESD Project Inventory in RCE Saskatchewan, Canada
Bibliographic record
Abstract
The Regional Centre of Expertise on Education for Sustainable Development in Saskatchewan (RCE Saskatchewan, Canada) is part of the United Nations University RCE Initiative in support of the UN Decade of Education for Sustainable Development (2005–14). With funding from the Government of Saskatchewan’s Go Green Fund, RCE Saskatchewan carried out research identifying education for sustainable development (ESD) projects within six priority areas for sustainability in its Canadian prairie region. This ESD capacity assessment was conducted by eight post-secondary students from late 2007 to 2009 and resulted in a searchable database and visual representation (map) of these ESD projects along with ongoing documentation of project milestones and processes. The database has become a useful tool assisting networking of Saskatchewan ESD providers, researchers and participants. This article describes the importance of the inventory in advancing the RCE, the project conception and management, the processes utilised for its successful completion (including descriptions of the technology utilised), the project findings and their implications. It concludes that for an RCE with minimal resources, an ESD project inventory employing student researchers within a higher education setting using Free/Open Source technologies is a cost-effective way of advancing the networking and capacity-building goals of an RCE.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".