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Record W2787476323 · doi:10.22215/etd/2014-10126

Cutting Knowledge: The Pathologization of Self-Injury in Correctional Discourse

2014· dissertation· en· W2787476323 on OpenAlexaffabout
Cristine Rotenberg

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsCarleton University
Fundersnot available
KeywordsConceptualizationCulpabilityIdeologyMental illnessPrisonPsychologyCriminologySocial psychologySociologyPoliticsGender studiesMental healthPsychiatryPolitical scienceLaw

Abstract

fetched live from OpenAlex

This thesis critically examines correctional discourse on prisoner self-injury produced by the Correctional Service of Canada's (CSC) Research Branch (RB) between 1990 and 2012.Since 2010, the RB has published over 700 pages worth of research on prisoner selfinjury.This new wave of research is identified in this thesis as a significant growth in correctional knowledge on self-injury.Self-injury in correctional environments has been a topic of much debate in recent years, where academics and governments alike have assessed the ability of the prison to adequately manage and treat self-injuring prisoners.Stepping back from these debates of the punitive versus therapeutic capabilities of the prison (Hannah-Moffat, 2001;Kendall, 1994;Kilty, 2006 Kilty, , 2008b)), this thesis examines what knowledges on prisoner self-injury are produced in correctional discourse and how these knowledges both enable and constrain understandings of self-injury in the prisoner population.Through a discourse analysis, this thesis identifies the discursive shifts in correctional knowledges on self-injury.Grounded in the extant clinical and correctional discourse that has historically constructed self-injury as practiced by manipulative, violent or suicidal prisoners, the new surge of research demonstrates a shift to predominately pathological explanations that aim to reduce the deviant behaviour to the manifestation of a mental illness.This domination of the 'psy-sciences' as 'intellectual technologies' (Rose, 1990(Rose, , 1996b) ) in turn both demotes and bars sociological or otherwise non-psy understandings of self-injury.It is suggested that political accountabilities further shape the output of research.This is demonstrated in how the characterization of prisoners as pathologically inclined to self-injure lends to the CSC's displacement of responsibility for self-injury from the prison to prisoners' mental illness.I suggest that women prisoners are increasingly pathologized for their self-injurious behaviours in relation to men when the historical medicalization of women's madness (Ussher, 1991(Ussher, , 2011) ) is considered.This thesis contributes to critical debates on whether quantitative versus qualitative methods should be problematized as contributing to the production of knowledge.It is argued that it is not the research methodology that should be problematized, but rather the ideological platform from which the discourse originates.It is suggested that regardless of methodological approach, the RB's conceptualization of prisoners who engage in selfinjury was pre-determined by their ideological alignment with the psy-sciences.The implications of the recent shift in correctional discourse with respect to the responsibilization of prisoners for their mental illness(es) are also explored.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.980
Threshold uncertainty score0.176

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.030
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.003
Science and technology studies0.0200.055
Scholarly communication0.0150.014
Open science0.0020.011
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.014
GPT teacher head0.358
Teacher spread0.344 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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

Citations1
Published2014
Admission routes2
Has abstractyes

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