CITIES AND THEIR GURUS: The Role of Superstar Consultants in Post‐political Urban Governance
Bibliographic record
Abstract
Abstract While consultants have crept into various aspects of municipal governance, a selected few have transcended the others reaching the status of urban gurus. Although consultants are often perceived as depoliticizing urban affairs, research shows that the urban guru often instigates politicization. Research on urban gurus does thus highlight distinctions between gurus and ‘lay’ consultants, but it has paid insufficient attention to describing how, through their interaction with cities, politicization occurs. Moreover, the literature often portrays this interaction as an authority relationship in which the guru is superior, while in fact cities play an important role in bestowing ‘guru’ status. Using fieldwork, I examine the long‐term interaction between Richard Florida and the City of Toronto, explaining how Florida's elevation to guru status by being brought to Toronto ended with him self‐describing as ‘persona non‐grata’. To explain the anomaly of this interaction and the way in which gurus instigate politicization, I differentiate between consultants’ ‘substance’ and ‘process’ roles in policy formulation processes. I show that, regarding substance, the guru offers a policy paradigm rather than policy instruments and, regarding process, their strength is in performing ideas rather than pulling strings behind the scenes—in both respects making the policy process more public and contested.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.024 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| 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".