‘Keep Them on the Straight and Narrow’: Understanding, Selecting and Governing Subjects Through Intensive Supervision Units
Bibliographic record
Abstract
Abstract Drawing from focus groups and semi-structured interviews, this paper examines decision-making practices and monitoring techniques of Canadian Intensive Supervision Units (ISUs) managing high-risk individuals in the community. We argue that ISU subjects are hyper-individualized through their unique conditions of release, contesting notions that actuarial risk assessments have eclipsed individual understandings of dangerousness in risk, correctional and policing literature. Using Foucault’s disciplinary, pastoral and confessional dispositifs, we highlight how ISU agents make subjects active participants in their own punishment. Moreover, we illustrate how dispositifs not only allow ISU agents to understand, select and govern subjects but also, more problematically, transform subjects into ostensibly dangerous entities reifying and necessitating escalating criminal justice interventions under auspices of protecting the community from potential—not guaranteed—harm.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".