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Record W6986928705

Reparative Engagments: A mad feminist approach to politicizing lived experiences of self-harm

2024· other· en· W6986928705 on OpenAlexaffabout

Bibliographic record

VenueYork University Digital Library (York University) · 2024
Typeother
Languageen
Field
Topic
Canadian institutionsWomen's and Gender Studies et Recherches Féministes
Fundersnot available
KeywordsNarrativeConversation analysisMental healthLived experienceConversationDiscursive psychologyPoliticsQualitative researchSocial constructionismEveryday life
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.230
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.006
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0030.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.004

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.

Opus teacher head0.022
GPT teacher head0.207
Teacher spread0.185 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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