Reparative Engagments: A mad feminist approach to politicizing lived experiences of self-harm
Bibliographic record
Abstract
This dissertation intervenes into dominant understandings of self-harm as a pathological problem behaviour in need of treatment and cure, opting, instead, to take as its point of departure an understanding of self-harm as a resourceful, multiplicitous, and socially embedded bodymind practice oriented towards attending to “what hurts”. Using a combination of critical qualitative methods (i.e., narrative inquiry and critical discourse analysis) this dissertation analyzes fourteen interviews with women, trans, and nonbinary adults living in Canada who identify with self-harm, placing interviewees' experience in conversation with the analysis of medical and cultural texts (i.e., psy- clinical literature, Canadian mental health policy documents, the DSM-5, and young adult novels pertaining to self-harm). Bringing together insights from mad studies, feminist disability studies, feminist theories of trauma, emotion, and embodiment, and social justice perspectives in mental health research, the dissertation pursues a deeply intersectional, situated, and reparative engagement with lived accounts of self-harm. This engagement critiques curative approaches to self-harm and works to position this practice beyond dualisms of ‘good’ or ‘bad’, choosing, instead, to conceptualize self-harm as something that both hurts and heals. The dissertation contributes to mad and feminist literatures an understanding of self-harm as a relational, political, and multiplicitous bodymind practice which is shaped by, and which responds to, the social, structural, and political contexts of everyday life.
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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 | 0.016 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.024 | 0.112 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.008 |
| 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".