Climate Justice in Urban Climate Policies in Canada and the United States
Bibliographic record
Abstract
Throughout the United States (US) and Canada, the effects of climate change have become apparent through rising temperatures, extreme precipitation, inland flooding, rising sea levels, air pollution, and the increased frequency and intensity of extreme heat. In the face of increasing challenges to health and security, cities in the US and Canada have begun to take action to combat climate change, including through urban design. This paper provides insight into how each country has approached climate justice in urban design through an in-depth analysis of the most recent climate action plans from major urban areas in each country. I explore differences in climate hazards and responses, as well as the communities or groups included or targeted in the planning process. The analysis reveals a concerning variation in the explicit mentions of vulnerable communities and groups among cities in the US and Canada, showing a lack of coordination within and between the two countries. It is also noted that more references are made to marginalized communities in climate action plans in the US than in Canada.
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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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".