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Record W2121582277 · doi:10.1177/10778010122183928

Healing the Wounds of Domestic Abuse

2001· article· en· W2121582277 on OpenAlexaff
Anita Sharma

Bibliographic record

VenueViolence Against Women · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsOppressionDomestic violenceSocioeconomic statusAffect (linguistics)Poison controlImmigrationSexual abuseDiversity (politics)MedicineSuicide preventionGender studiesCriminologySociologyPsychologyPsychotherapistPopulationPolitical scienceMedical emergencyEnvironmental health

Abstract

fetched live from OpenAlex

Feminist therapy is a powerful means of helping abused women because of its focus on systemic sources of oppression and hegemony. These forms of oppression are intimately linked to a woman's cultural and socioeconomic background and affect the way she experiences domestic abuse. Although researchers have shown that feminist therapy can be an effective approach for counseling abused women, it is criticized here for failing to acknowledge the diversity among immigrant and racially visible women who have been abused. The author will examine both the effectiveness of feminist therapy and its limitations when counseling immigrant and racially visible women who have been abused. The author will also describe the reality of domestic violence among these women and how their needs differ from women in the dominant culture. Strategies for a more inclusive form of feminist therapy will also be offered as alternatives to existing modes of practice and assessment.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.005
Scholarly communication0.0030.002
Open science0.0000.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.020
GPT teacher head0.316
Teacher spread0.297 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations77
Published2001
Admission routes1
Has abstractyes

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