Exploring Practitioner Responses to Green Gentrification in Toronto, Canada
Bibliographic record
Abstract
Cities are adapting to and mitigating the negative effects of climate change, but recent academic literature suggests that environmental efforts could undermine social stability through a process known as ‘green gentrification.’ This research builds on existing case-based studies on green gentrification by exploring practitioner understanding and responses to green gentrification in Toronto, Canada. Through practitioner interviews, an informal content analysis, and a literature review, this study analyzes how practitioners understand green gentrification, how they attempt to prevent it, and what they can do to better prevent it. The study finds that most practitioners did not have significant recognition of green gentrification, but they did intuitively understand it. Practitioners did not identify many mitigation tactics currently used but brainstormed ways that negative effects could be mitigated in the future. These ideas, combined with existing evidence, were consolidated into best practices that may help practitioners mitigate the negative consequences of green gentrification.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.020 | 0.011 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".