Marketisation or Corporatisation? Making Sense of Private Influence in Public Policing Across Canada and the Us
Bibliographic record
Abstract
Introduction In 2015, one of us attended a conference in the United Kingdom on policing and markets. Coming from Canada, the tone and tenor of the discussion about the expanding role of the private sector in policing was striking. For most attendees from the public police and the security industry blending in among the academics, private penetration of the public policing realm was deemed a fait accompli. Conference goers used phrases such as ‘the ship has sailed’ and ‘the genie is out of the bottle’, to refer to seemingly irreversible inroads of private security into police practices. Correspondingly, security industry representatives at the event made formal and informal pitches to woo public police, seeking to sell cost-saving packages and security management solutions, and encourage administrators to further divest. There was discussion of ‘core tasks’ of criminal justice and how to create ‘efficiencies’ via privatisation (Hancock, 1998). Notable criminologists (for example Spitzer and Scull, 1977; Shearing, 1992) have been alerting scholars and seeking to make sense of this trend for some time. For us, the shocking part of observing these UK developments first-hand is that in Canada the public police presence is not receding. Instead, public police budgets are mostly growing; public personnel numbers are not in sharp decline. Though there is some mild civilianisation, there is no hollowing out, and there are few public–private partnerships. There are no scenarios like in the UK where entire front and back offices of police are outsourced to G4S (Dehaghani and White, Chapter 7, this volume). Currently in Canada that is unfathomable. Police are starting to charge ‘users’ for some items but are not selling off the institution to the private sector. Of course, Canada has a robust private security industry, with private security personnel easily outnumbering public police several fold. Yet the status of private security is much lower, and private security is thought to be distinct from public police even though some private security personnel occasionally seek to act as public police. Though Canadian governments have outsourced and privatised numerous other Crown and state entities, such as energy and telephone ministries, there is little appetite in Canada to apply those ideas to police.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.028 | 0.028 |
| Scholarly communication | 0.019 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".