A critical ethnographic perspective on risk and dangerousness in forensic psychiatry
Bibliographic record
Abstract
In the Canadian forensic psychiatric context, the concepts of risk and dangerousness interact, intersect, and morph into the notion of significant threat to the safety of the public. Stemming from the results of a critical ethnography of the Ontario Review Board, this article unpacks the central role of forensic psychiatric nursing, as an example of a 'psych' discipline (e.g., psychiatry and psychology), in a system that is built to produce risky persons and to legitimize their detention and supervision. By using excerpt of interviews conducted with nurses, ethnographic observations of Review Board hearings, and other documentary artifacts, the findings illustrate how rationalizations of risk and dangerousness are contingent on space, time, and observer. Depending on the time of the assessment or on the health-care professional who performs it, different elements including, but not limited to, mental illness, interpersonal relationships, financial instability, and sexual vulnerability, are relied upon in very fluid, interchangeable, and discretionary ways to justify findings of dangerousness. Such a dynamic expands the reach of psychiatry's legitimacy at identifying risky conduct and controlling risky persons to domains very loosely associated with the notion of dangerousness. The work of Foucault and Castel provides the theoretical backdrop on which rests the discussion and the implications for nursing.
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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 | 0.026 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.041 | 0.078 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| 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".