Bibliographic record
Abstract
Rather than relying on an undifferentiated version of Michel Foucault's panopticon or conceptualizing surveillance as a straightforward top-down measure, this article contends that open-street closed-circuit television (CCTV) surveillance is generated from numerous and overlapping social positions. As a regulatory project within the overarching context of governance, open-street CCTV can be generated from above, from the middle, or from below. By "above," what is meant is some hierarchical political or administrative body. Business entrepreneurs constitute the "middle." By "from below," I mean that citizens themselves seek out regulatory measures for their own communities through moral entrepreneurship, often in collusion with local news media. But the inverse is also true: power moves through populations, and thus citizens' groups have the power to contest regulatory measures in their communities. I substantiate these theoretical claims with media, questionnaire, and interview data regarding the proliferation of open-street CCTV in Canada. Drawing from the sociologies of governance, of risk, and of critical media studies, and offering a more nuanced theoretical trajectory than theories that reproduce top-down conceptualizations of power, politics, and communication, I challenge the reigning theoretical models pertaining to open-street CCTV surveillance so as to demonstrate how regulation through camera surveillance can be generated from any number of social positions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".