1 Charting the Landscape: An Overview of Sustainability Education in Canadian and US Higher Education
Bibliographic record
Abstract
hoW caN socieTy address wicked social and environmental problems and transition to a more sustainable future?While there is no easy answer to this crucial question, many scholars and practitionersincluding the authors of this book-argue that higher education plays a key role in tackling sustainability challenges (Stephens and Graham 2010).This chapter highlights how a growing number of higher education institutions (heis) in Canada and the United States acknowledge this role through their policies and practices.It outlines how they bring sustainability into their institutional frameworks by signing declarations or embedding sustainability in institutional policies.It reviews several approaches taken to integrate sustainability in their formal education systems, such as creating sustainability courses and programs and adopting learning approaches to teach sustainability more effectively.The chapter briefly describes how heis link sustainability teaching to both research and campus operations through initiatives like campus as a living lab, then outlines some of the ways sustainability is implemented in outreach and collaboration.Finally, it touches on the progress that a few heis are making in integrating sustainability throughout their systems.
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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 | 0.001 | 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) | 0.001 | 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".