Expanding our understanding of sovereign power: on the creation of zones of exception in forensic psychiatry
Bibliographic record
Abstract
The purpose of this paper is to engage with the readers in a theoretical reflection on nursing practices in forensic psychiatric settings. In this paper, we argue that practices of exclusion in forensic psychiatric settings share some common ground with Agamben's description of sovereign power and, consequently, the possible creation of zones of exception in this environment. The concept of exception is, therefore, purposely used to shift our thinking, highlight the political forces surrounding exclusionary practices in forensic psychiatric nursing, and explore the ethical tensions that arise for nurses who become entangled in the control of these zones of exception. We argue that ethical and political discussions regarding this phenomenon are necessary if we wish to further understand the complexities of this field of practice and promote constructive change.
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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 | 0.021 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.159 |
| Scholarly communication | 0.016 | 0.027 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".