Risk Classification and Municipal Policing in Canada: Altered Practice and Innovation
Bibliographic record
Abstract
In the current Canadian carceral system, categories of risk related to reoffending are assigned to individuals on the basis of their behaviours and traits inside and outside carceral institutions.This dissertation examines the complex history and application of how classifications are generated in order to understand how the police take up these classifications in their work toward ensuring public safety as they manage and supervise certain high risk individuals in the community.An 'action in practice' methodology (actor network theory) was adopted in this project whereby voluminous governmentproduced records were analyzed and interviews were used to capture the experiences of police officers in Edmonton, Alberta, Canada, and how their work relates to risk classification practices for the management and supervision of individuals classified as 'high risk to reoffend' in the community.This research captures how police officers selfidentify and define their roles in risk classification practices through which both actions of replication of carceral risk classifications and altered policing practices have emerged.I am grateful to all of those with whom I have the pleasure to work during this and other related projects.Specifically
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.020 | 0.016 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".