Exploring the resilience movement through an urban planning and water management lens
Bibliographic record
Abstract
Land use planning recognizes the need for incorporating climate change adaptation strategies to address natural disaster reoccurrence. In 2013, the Rockefeller Foundation developed the 100 Resilient Cities (100RC) model to support initiatives related to climate change and resilience. Globally through the model, cities appointed Chief Resilience Officers (CROs) to develop a vision, lead implementation and establish long-term city resilience. Three major cities in Canada (Toronto, Vancouver and Montreal) are now RC100 cities and subsequently introduced the positions of CROs. The purpose of this research paper is to highlight the current state of interventions in Toronto water management strategies to emphasize the role land use planning can have in Resilience Strategy development. Recommendations will be made based on literature review, policies and best practices scan, as well as stakeholders’ interview analysis. Safety and wellbeing of citizenry are at the forefront of the urban agenda, requiring utmost attention to climate change and precautionary measures against natural disaster. Key words: land use planning, urban water management, Canada, resilient cities
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.009 | 0.035 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".