Infrahuman madness: Mental health nursing and the discursive production of alterity
Bibliographic record
Abstract
By examining an exemplar sample of mental health nursing educational policies and related legislation, in this article, we trace the discursive production of madness as an "othered" identity category. We engage in a critical discourse analysis of mental health nursing education in Canada, drawing on provincial and federal policies and legislation as the main sources of data. Theoretically framed by critical posthumanism and mad studies, this article outlines how the mad subjectivity becomes decontextualized out of its identity-based understanding and recontextualized as an inferior category of "the human," circulating within discourses of pedagogy, economics, law, and psychiatry. The article maps the intertextual nexus of the discourse of mental health nursing education, making visible the complex, the arbitrary, and the sometimes-contradictory nature of the discipline's grappling with identity-based mental health concepts. We close with several implications for nursing policy, education, and practice.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".