Repowering Cities: Governing Climate Change Mitigation in New York City, Los Angeles, and Toronto
Bibliographic record
Abstract
City governments are rapidly becoming society's problem solvers. As Sara Hughes shows, nowhere is this more evident than in New York City, Los Angeles, and Toronto, where the cities' governments are taking on the challenge of addressing climate change.Repowering Cities focuses on the specific issue of reducing urban greenhouse gas (GHG) emissions, and develops a new framework for distinguishing analytically and empirically the policy agendas city governments develop for reducing GHG emissions, the governing strategies they use to implement these agendas, and the direct and catalytic means by which they contribute to climate change mitigation. Hughes uses her framework to assess the successes and failures experienced in New York City, Los Angeles, and Toronto as those agenda-setting cities have addressed climate change. She then identifies strategies for moving from incremental to transformative change by pinpointing governing strategies able to mobilize the needed resources and actors, build participatory institutions, create capacity for climate-smart governance, and broaden coalitions for urban climate change policy
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".