Direction, drivers and design: The driving forces of sustainability policy creation at Canadian universities
Bibliographic record
Abstract
Internationally, pushes for sustainability have come from a variety of actors, and universities have increasingly been seen as important sustainability actors due to their roles in research, education and their large resource consumption. Given this context, it is important to understand how universities shaped their sustainability direction. The aim of this research then was to explore the drivers that result in universities' sustainability direction. In order to do this, interviews were conducted with 16 university sustainability administrators from across Canada to determine the drivers of their sustainability strategy and direction. This research adopted a mixed methods coding procedure to analyse the interviews. Findings include that internal drivers including senior administrators and community members are the most significant drivers of sustainability priority development, and that government legislation has minimal impact on university sustainability priorities. Universities highlight that global governance mechanisms such as ratings are seen as both legitimate and useful, however high-level benchmarks and ranking organizations are often seen as communications tools rather than drivers. This research presents one of the earliest of its kind focused on the drivers of action on sustainability in higher education and shows how policy development takes a unique approach in universities compared with other sectors. These findings are significant to understand how universities are shaping their sustainability direction and to support policy makers and practitioners to guide university sustainability towards meaningful planning and prioritization. Conclusions include a need for future research into the implementation into such policies and a focus on the ‘next generation’ of sustainability priorities in new emerging policies.
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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 | 0.024 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.027 | 0.014 |
| Scholarly communication | 0.025 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".