Privatizing Security, Securitizing Policing: The Case of the G20 in Toronto, Canada
Bibliographic record
Abstract
Allegations of police brutality, unlawful detention, and other breaches of civil liberties during the G20 in Toronto in June 2010 provide an important case through which to understand the changing nature of security and policing, raising questions about the political implications of such shifts in terms of police accountability, transparency, and democracy. Within the field of public policing, scholars predicted that globalization processes would weaken public policing as a dominant policing institution. Instead, it has expanded, in part, through the convergence of internal and international dimensions of security, whereby new policy networks cooperate in matters of policing and security in a new integrated model, the result of which is a further militarization of urban space and expanded markets for security, leading to the securitization of everyday life. This article examines the case of Toronto's hosting of the G20 and the role that the Integrated Security Unit—led by the RCMP and including private security firms—played. By focusing on the role of multilateral networks that include private sector actors, we examine the implications of the privatization and securitization of policing for democracy, citizenship, and accountability, looking at how they affect the ability of publics to engage in public debate, to consult, or to protest policies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.040 | 0.010 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 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".