Are Canadian Universities Taking Sustainability Seriously? A Case Study Analysis of Sustainability Initiatives at Three Canadian Campuses and the Lessons Decision-Makers Can Learn from These Efforts
Bibliographic record
Abstract
This study attempts to answer the question ‘are universities taking sustainability seriously’? Specifically, this paper examines the extent higher education institutions have institutionalized sustainability into their decision-making processes. It investigates whether universities have incorporated a comprehensive conception of sustainability into visioning, planning, operations, and administration. It also attempts to determine how educational institutions have gone about institutionalizing sustainability and glean best practices that may be applied in other institutional settings. In order to answer the above questions, I present three case studies of sustainability policies and efforts at the following Canadian universities: The University of British Columbia, The University of Calgary, and Carleton University. These case studies examine the plans, actions, and progress at each university to determine if they sufficiently address sustainability considerations. Case studies also attempt to take stock of campus sustainability efforts and highlight achievements and deficiencies. Subsequently, the experience of universities is utilized to uncover and discuss lessons for policymakers and barriers to sustainability. Finally, recommendations are made for overcoming barriers.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.023 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".