Encouraging green infrastructure at Ontario universities: What's policy got to do with it?
Bibliographic record
Abstract
In this paper, and via a case study in Waterloo, we explore policy's role in encouraging green infrastructure (GI) adoption in Ontario universities. More specifically, we evaluate the relationship between policy and GI, and determine the policy level required to successfully implement GI. We employed a qualitative research approach of semi-structured, open-ended interviews (n = 8) to understand better participants' views towards existing GI policies and frameworks. We find that multi-level government collaboration, regulatory frameworks and incentives and funding mechanisms are key themes influencing GI adoption. Interviews revealed that municipal incentives are essential in encouraging GI implementation on a local scale. However, federal and provincial factors are also crucial for the long-term establishment of GI. We conclude that policy is essential, and that multi-level collaboration is required to implement GI across Ontario's universities. With little published research there is in this area suggests the importance of government policy, especially at the municipal level, in terms of getting GI projects built. Yet, there are key gaps in our understanding, including the role of provincial and federal policy.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".