Municipal Corporate Security and the Intensification of Urban Surveillance
Bibliographic record
Abstract
This article explores the surveillance work of municipal corporate security (MCS) units in Canadian cities. Drawing on analysis of freedom of information requests, we document the introduction of new and modified surveillance technologies through MCS. These units engage in surveillance of City employees and citizens on municipal lands and in municipal buildings. Although some technologies deployed by MCS (such as electronic access cards and badges) appear mundane, we demonstrate how MCS units are repurposing, enhancing, and recombining these technologies with existing forms in ways that have been described as the intensification of surveillance. While recent attention in the surveillance studies and urban studies literatures has been rightfully placed on private auspices and provision of externally directed urban surveillance, our analysis of MCS activities suggests that scholars should continue to focus on public auspices and provision of security and internally directed surveillance too. What defines the intensification of urban surveillance therefore may be less a privatized and technologically advanced character and more a resolute comfort with a constantly mutating amalgam of public/private, human/technological, and external/internal forms and foci.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".