Carceral Politics, Inpatient Psychiatry, and the Pandemic: Risk, Madness, and Containment in COVID-19
Bibliographic record
Abstract
In this paper, we discuss how the COVID-19 pandemic offers a particularly salient moment in which to identify and reflect on shifts in psychiatric carcerality in highly concrete ways. Drawing from our own professional and practical experience as in-patient (acute-care) psychiatrists implementing changes in ward policies in light of infection control concerns and linking this experience with insights and tensions between Mad Studies, Critical Prison Studies, and the psychiatric writings of Franz Fanon, we focus on specific ways that therapeutic value is undermined within these complicated and complex settings. Using Repo's metaphor of “carceral layers,” our analysis considers how particular infection control policies and practices, institutional approaches to pandemic management, and larger ideologies of risk have worked together to produce spatio-temporal aspects of carcerality in a psychiatric acute-care setting in Toronto, Canada.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".