Green Dreams, Concrete Realities: Overcoming Climate Hurdles in American Middle Cities
Bibliographic record
Abstract
American middle cities (those between 50,000 and 1 million residents) are home to a quarter of all Americans and are on the frontlines of climate change, yet they are largely excluded from the literature on urban climate politics. At the same time, while the literature highlights the importance of state institutions, state capacity, and environmental coalitions, less is known about how these factors interact with local political and economic conditions. I advance the field by directly considering these local contextual features through original ethnographic fieldwork in four middle cities. I find that the composition of coalitions both for and against climate policies can differ widely based on a city’s economic base and racial homogeneity. In addition, analysis of these middle cities demonstrates a challenge in overcoming the physical legacy of industrialization. Taken together, this paper sheds new light on urban environmental politics by focusing on a category of understudied cities that house a large section of the country’s population and economic output, while also being home to some of its most economically and environmentally disadvantaged communities.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.013 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".