“Martial Law in the Streets of Toronto”:G20 Security and State Violence
Bibliographic record
Abstract
This paper examines the events, microgeography and broader context of the effective siege of downtown Toronto by Canadian security forces during the June 2010 meeting of the G20, and the unprecedented assault on peaceful protestors and innocent bystanders alike. An extraordinary clampdown of Toronto streets was organized by integrated security forces at the international, federal, provincial and local scales, leading to the arrest and jailing of a larger number of people (overwhelmingly released without charges) than in any other event in Canadian history. Whereas popular consternation emerged immediately against police brutality with many commentators aghast that this could happen in “Toronto the good,” suggesting that this represented an exceptional event, this paper argues that to a significant degree the crisis in the streets was precipitated by the security forces themselves, an argument buttressed by the refusal of the Canadian government to investigate the events. The paper connects the G20 to the larger issues of global political economic power and urban securitization, and puts the Toronto G20 police riot against protestors, if that is what it was, in the context of state power and the state's claimed monopoly over violence. Far from an exceptional event, this repressive assault expressed the DNA of capitalist state behavior and the selectivity of its targeted social violence.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.017 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".