Peripheral recovery: ‘Keeping safe’ and ‘keep progressing’ as contradictory modes of ordering in a forensic psychiatric unit
Bibliographic record
Abstract
Sitting between the psychiatric and criminal justice systems, and yet fully located in neither, forensic psychiatric units are complex spaces. Both a therapeutic landscape and a carceral space, forensic services must try to balance the demands of therapy and security, or recovery and risk, within the confines of a strictly controlled institutional space. This article draws on qualitative material collected in a large forensic psychiatric unit in the UK, comprising 20 staff interviews and 20 photo production interviews with patients. We use John Law’s ‘modes of ordering’ to explore how the materials, relations and spaces are mobilised in everyday processes of living and working on the unit. We identify two ‘modes of ordering’: ‘keeping safe’, which we argue tends towards empty, stultified and static spaces; and ‘keep progressing’ which instead requires filling, enriching and ingraining spaces. We discuss ways in which tensions between these modes of ordering are resolved in the unit, noting a spatial hierarchy which prioritises ‘keeping safe’, thus limiting the institutional capacity for engendering progress and change. The empirical material is discussed in relation to the institutional and carceral geography literatures with a particular focus on mobilities.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.056 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".