Documenting the Document: The Forensic Hospital Report and Its Knowledge Moves
Bibliographic record
Abstract
Drawing on case files from a Canadian provincial review board tasked with determining the disposition of persons found ‘not criminally responsible on account of mental disorder’, we explore the role of the forensic hospital report in the production of medico-legal risk knowledges. Through a detailed case study, we show how the report's content and particular material form allow the Board to produce the ‘significantly threatening individual’ – the very thing the Board (and report) are meant to presuppose. We therefore call on scholars to document their documents, and, in the spirit of actor-network theory (ANT), to analytically treat socio-legal objects as active participants in knowledge's creation. By accounting for the ‘knowledge moves’ the hospital report might allow, encourage, or prohibit human actors to make, we hope even ANT sceptics can use these tools to better understand various legal decision-making processes and their effects.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".