Intelligence-Captivated Policing: Real-Time Operations Centres and Real-Time Situational Awareness in Canadian Police Services
Bibliographic record
Abstract
Over the last decade a new organisational entity has appeared in Canadian police forces. Based on post-9/11 US homeland security and developments in digital surveillance technology, real-time operations centres (RTOCs) have quietly sprung up in cities from Vancouver to Halifax. Sold as providing digital backup to responding officers, they are fundamentally restructuring how data is acquired and used and shifting the relations of power between police and citizens. \n\tBased on interviews, site visits, access to information requests, and various international trade shows, this thesis investigates the inception, development, operation, and likely effects of RTOCs on policing. It first deploys an STS framework to unravel the processes of heterogenous engineering and sociotechnical co-construction involved, and identifies ‘real-time situational awareness (RTSA)’ as the sociotechnical imaginary at the heart of the RTOC project. \n\tOn this basis, the thesis then isolates the modally distinct yet operationally interconnected dispositifs of surveillance, power, and policing at work in RTOCs: a pre-emptive, yet event-based and hyper-reactive, risk-driven surveillant assemblage that serves to extend policing into new digital data spaces. It then draws on research into analogous surveillance systems to postulate the likely dangers involved: hypervisibilization, mission creep by design, context collapse, and the reification of racist, classist, or gender-based prejudices in novel sociotechnical procedures. The thesis then places these conclusions within the discourse on intelligence-led and big data policing to identify a fundamental shift in the operational logic of police intelligence work caused by RTOCs and RTSA towards the prioritization of tactical reactivity and immediately actionable intelligence over strategy and preemption, as well as non-consensual or exploitative information collection over transparent or democratic methods. \n\tFinally it argues that, in terms of progressive resistance and reform, this research makes it clear that police forces are struggling to develop a response to the ongoing digitalisation of society, and have become captivated by the only model on their horizon: the military and counter-terrorism theory of RTSA. Prohibiting individual technologies will not prevent the hegemony of this rationality: we need alternative systemic approaches to citizens, data, and governance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 teacher head, 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".