Issue Proximity and Policy Response in Local Governments
Bibliographic record
Abstract
Abstract The policy choices of local governments are highly relevant today, but we know relatively little about how or when local governments choose to respond to a given issue and why this might vary between policy areas. A key variant for local governments is the proximity of policy issues: they are engaged in solving local, regional, and global problems. Using evidence from the United States on the policy issues of social inclusion, watershed management, and climate change, we demonstrate that the drivers of policy response vary with the proximity of the problem. When an issue is highly local, policy response is influenced by problem severity; when an issue is global, policy response is influenced by local political leanings; and when an issue is regional, policy response is driven by the actions of neighboring and state level governments. Local governments consider different factors and respond to different cues when engaging with different types of policy issues. Our findings provide a more nuanced understanding of sustainability policy adoption in local governments, and further our understanding of the domain‐contingent nature of policy response in local governments and the structuring role of problem proximity.
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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 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".