The Local Politics of Policy Mobility: Learning, Persuasion, and the Production of a Municipal Sustainability Fix
Bibliographic record
Abstract
The authors draw on the concept of a ‘sustainability fix’—a political discourse which allows development to proceed by accommodating both profit-making and environmental concerns—to analyze how municipalities muster support for development in the face of worries about negative environmental impacts. The case of Whistler, British Columbia, a tourist resort with an official orientation toward sustainable development, is used to illustrate the politics of balancing economic and environmental commitments. The authors deepen the sustainability fix concept by addressing: first, how such a fix is achieved through the assemblage of local and extralocal resources—specifically, ‘imported’ policy models which direct attention to certain definitions of problems and legitimate specific types of policy solutions; and second, how the politics of municipal policy-making is about more than contention and how it involves the sort of ongoing and broadly defined learning that has been largely undertheorized in the local politics literature. A key point is that local politics and policy making are always also extralocal in various ways. They involve a local politics of policy mobility. The authors expand on this premise to show how Whistler's model of sustainability planning has recently been circulated to other municipalities with similar social, economic, and environmental conditions.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.041 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".