How is self-mutilation constructed? An examination of discourses of gender, thebody and risk in the DSM and by psychiatrists
Bibliographic record
Abstract
My research is concerned with the production of knowledge and how the process of knowledge production might shape how we view and understand people’s bodies. In particular, this research sought to understand the construction of knowledge about selfmutilation, how discourses of gender, the body and risk shaped how self-mutilation was perceived and whether or not these dominant knowledge(s) re-produced inequalities. The aim of my research was to explore the various ways of thinking that surround selfmutilation and to map the connections and disconnections between the diagnostic criteria used to diagnose self-mutilation and psychiatrists’ understandings. Using a poststructuralist critical discourse analysis approach, I conducted a longitudinal analysis of the Diagnostic and Statistical Manual (DSM) versions 1 through 5 (spanning 1952-2013) and in-depth interviews with ten psychiatrists practicing child, adolescent and adult psychiatry. The results illustrate that knowledge produced in the DSM does impact how psychiatrists make sense of self-mutilation. Drawing on multiple theoretical perspectives, such as the work of Deborah Lupton, Michel Foucault and Dorothy Smith, I show that self-mutilation discourses reflect larger dominant ideas surrounding gender, the skin, healthy bodies and risk; that self-mutilation is gendered and is linked to a diagnosis of borderline personality disorder; and that there are multiple ways in which DSM language is taken up, reproduced and resisted by psychiatrists. In sum, this thesis has outlined the intersections between gender, power, and psychiatric knowledge.
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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 | 0.024 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.016 | 0.045 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.001 | 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".