Bibliographic record
Abstract
This study develops the notion of “policy boosterism,” a subset of traditional branding and marketing activities that involves the active promotion of locally developed and/or locally successful policies, programs, or practices across wider geographical fields as well as to broader communities of interested peers. It is argued that policy boosterism is (1) an important element of how urban policy actors engage with global communities of professional peers and with local residents, and (2) a useful concept with which to interrogate and understand how policies and policy knowledge are mobilized among cities. A conceptualization of policy boosterism and its role in global-urban policymaking is developed by combining insights from the burgeoning “policy mobilities” literature with those of the longstanding literature on entrepreneurial city marketing. It is supported by illustrative examples of policy boosterism in Vancouver: the city's Greenest City and Green Capital initiatives, the use of the term “Vancouverism” among the city's urban design community, and demonstrations of new urban technologies during the 2010 Winter Olympics that were used to market a particular vision of the city's governance to people from elsewhere, but also—crucially—to local audiences. The article concludes by highlighting four foci that might frame future work at the intersections of policy boosterism and policy mobilities.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".