Planning for Resilient Water Infrastructure: Understanding the Water System and the Impacts of the Planning Process in Implementing Green Infrastructure Projects within Ontario Municipalities
Bibliographic record
Abstract
This Major Paper examines how green infrastructure has been incorporated in Ontario municipalities and the barriers and challenges associated with its planning and implementation. Based on two Ontario municipalities, the City of Toronto and Brampton, this paper argues that while municipalities have begun to integrate green infrastructure into their planning practices, issues around weak policy, knowledge and training, senior management buy-in and risk aversion, as well as collaboration and public acceptance have affected these municipalities’ abilities to implement green infrastructure projects on a municipal-wide scale. Through qualitative interviews with key practitioners (n = 6), solutions to address these challenges are identified. This paper argues that implementing strong green infrastructure policies, providing greater training opportunities, gaining senior management buy-in, developing a dedicated, interdisciplinary leadership team, and creating new approaches to educate the public are essential next steps. By working towards these solutions, municipalities will be able to begin working towards fully integrate green infrastructure into the planning process, inherently make green infrastructure visibly dominant and increasing the resiliency of the water network.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".