(Dis)Assembling policy pipelines: unpacking the work of management consultants at public meetings
Bibliographic record
Abstract
Abstract. Confronting growing fiscal deficits in the wake of the 2008 economic crisis, local governments around the world have often commissioned outside experts – such as policy gurus, management consultants, and transnational professional service firms – to undertake services delivery reviews as a means of making tough decisions, identifying the areas of government spending that are most expendable and setting priorities for cutbacks. This paper draws from the recent literature on trans-urban policy pipelines in studying the role of service delivery reviews in thickening relations of knowledge production between city regions (McCann and Ward, 2011, 2013; Prince, 2012). Taking the encounter with Toronto's 2011 Core Service Review as a starting place, it sets out to examine the textually mediated practices through which policy knowledge is generated. Drawing from Allen and Cochrane's (2010) topological approach, it highlights how management consultants make use of evaluative texts, lifting out and folding in knowledge and ideas from other places to make their presence felt. However, while these texts are presented as a pure lens of cost savings, the work of rendering the city of Toronto commensurate with other distant places is often based on fragile and tenuous connections. Hence, against assumptions that these texts facilitate the foreclosure of possible utterances that can be made, I also explore the public meeting as a site for investigating alternative ways of knowing the city, providing a window onto the way in which oppositional registers of the city are themselves generated through translocal practices.
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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 | 0.051 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.041 | 0.040 |
| Scholarly communication | 0.030 | 0.020 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".