How Municipal and Regional Police Align Their Practices with Canadian Security Intelligence Agencies: A Study in Nodal Governance
Bibliographic record
Abstract
This thesis explores the inter-agency cooperation between municipal and regional police agencies and security intelligence agencies, as it seeks to answer the overarching research question: How have municipal and regional police agencies in Ontario aligned their practices with security intelligence agencies in Canada? The goal is to explore and lend insight into how municipal and regional police agencies cooperate with security intelligence agencies, such as the Canadian Security Intelligence Service (CSIS) and the Royal Canadian Mounted Police (RCMP) to address national security concerns. This thesis uses the concept of nodal governance (Shearing & Wood, 2003) to discuss any partnership or cooperation, as well as David Garland’s (1996) concept of ‘responsibilization’ from the nodal governance and related governmentality literatures (Lippert & Stenson, 2010). Some investigative alignment will be discerned using publicly available documents. The findings will also analyze the general nature of relations, any inter-agency misalignment and conflict, the alignment of training techniques, the alignment of mentalities, and the use of central intelligence hubs and/or communication formats (Ericson & Haggerty, 1997).Keywords: national security, intelligence-led policing, inter-agency cooperation, information-sharing, interoperability, and collaboration
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.028 | 0.010 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".