Bibliographic record
Abstract
Purpose The purpose of this paper is to describe the role that the University of Toronto has had in helping to establish a Regional Centre of Expertise (RCE) on Education for Sustainable Development in Toronto, Canada. The way in which the RCE initiative has helped to move forward the university's own five‐year plan will also be discussed. Design/methodology/approach The paper begins with a historical overview of the development of the Toronto RCE, acknowledging the diverse range of NGO, governmental and educational institutions that collaborate within this network. It then describes how the RCE initiative is helping to advance the objectives of the University of Toronto's own five‐year plan. Finally, the paper details how the University of Toronto has supported specific projects of the RCE, and where it hopes to help to lead the RCE into the next phase of its development. Findings In addition to presenting a case study of an RCE, the paper includes critical discussion of broader conceptual issues, such as how one might best interpret “interdisciplinarity” and community “outreach” in a university setting. Originality/value The UN University's RCE is, in itself, a highly original and valuable initiative. The paper describes one of these networks and its own, unique focus, while also drawing conclusions about how universities might more actively engage with community partners to advance environmental awareness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.042 | 0.004 |
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".