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Record W4398249869 · doi:10.22329/csw.v24i1.8073

Troubling White Women’s Desire for Innocence in Community-Based Social Work Practice: A Foucauldian Discourse Analysis

2023· article· en· W4398249869 on OpenAlexaffvenueabout
Jill Hoselton, Christine A. Walsh

Bibliographic record

VenueCritical Social Work · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsInnocenceWhite (mutation)SociologyGender studiesWork (physics)Discourse analysisSocial analysisPsychoanalysisSocial psychologyPsychologySocial scienceLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

Understanding the effects of White femininity on social work practice is a worthy endeavour provided the disproportionate numbers of White women that have built and occupied the professional role of social worker from the origins of colonization to present. Mainstream accounts of social work history narrate the profession to be a pursuit of well-intentioned middle- and upper-class White women, concealing the colonial operations that underpin the formation of social work as a White feminine project. While Whiteness and its effects within social work have been a subject of study, the intersection of gender and Whiteness has been minimally addressed, disregarding significant nuances that inform the relationship between the project of colonization and social work. This paper provides a detailed analysis of semi-structured interviews with eight White, woman-identified social workers who have practiced community-based social work for a minimum of two years in Alberta, Canada. Foucauldian discourse analysis is used to trace the ways the participants perform the dominant discourse of White femininity. The findings illuminate the social work profession's ongoing complicity with its colonial origins, which collude with White femininity and ultimately foster practices steeped in racism. A vital need to disentangle social work from its colonial and racist scripts is emphasized.

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)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation 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 score0.297
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.019
Science and technology studies0.0070.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.082
GPT teacher head0.434
Teacher spread0.352 · 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 teacher head, 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

Citations3
Published2023
Admission routes3
Has abstractyes

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