Expertise, Truth, and Urban Policy Mobilities: Global Circuits of Knowledge in the Development of Vancouver, Canada's ‘four Pillar’ Drug Strategy
Bibliographic record
Abstract
There is growing attention across the social sciences to the mobility of people, products, and knowledge. This entails attempts to extend and/or rework existing understandings of global interconnections and is reflected in ongoing work on policy transfer—the process by which policy models are learned from one setting and deployed in others. This paper uses a case study of the development of an innovative approach to drug policy in Vancouver, British Columbia to deepen our understanding of what I call ‘urban policy mobilities.’ It details the often apparently mundane practices through which Vancouver's ‘four-pillar’ drug strategy—which combines prevention, treatment, enforcement, and harm reduction—was learned from cities outside North America and is now increasingly taught elsewhere. In doing so it draws on a neo-Foucauldian governmentality approach to emphasize the role of expertise (specialized knowledge held by many actors, not just credentialed professionals) and the deployment of certain powerful truths in the development of the policy. The paper concludes by discussing the spatialities of urban policy mobilities and raising questions about the political and conceptual importance of also maintaining a focus on the causes and consequences of policy immobilities.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.027 | 0.035 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".