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Record W2528052803 · doi:10.20381/ruor-3567

Racialized Immigrant Women Responding to Intimate Partner Abuse

2014· dissertation· en· W2528052803 on OpenAlexaboutno aff
Christeena Lucknauth

Bibliographic record

VenueuO Research (University of Ottawa) · 2014
Typedissertation
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationDomestic violencePsychologyCriminologySocial psychologyGender studiesSociologyPolitical scienceMedicineSuicide preventionPoison controlMedical emergency

Abstract

fetched live from OpenAlex

This exploratory study investigates how racialized immigrant women experience and respond to intimate partner abuse (IPA). The American and European models of intersectionality theory are used to highlight structural constraints and agentic responses as experienced and enacted by racialized immigrant women. Eight women described their experiences through semi-structured interviews, revealing an array of both defensive and pro-active types of strategies aimed at short- and long-term outcomes. Responses included aversion, negative reinforcement or coping strategies like prayer or self-coaching, and accordingly varied by the constraints under which the women lived as newcomers to Canada. Policy recommendations promote acknowledgement of women’s decision-making abilities and provide a model in which women can choose from a selection of options in how to respond, rather than strictly interventionist models. Study results can help to challenge stereotypes of abused women as passive victims, and empower the image of immigrant women as active knowers of their circumstances.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.986
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.002
Scholarly communication0.0020.001
Open science0.0000.004
Research integrity0.0010.001
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 head0.044
GPT teacher head0.383
Teacher spread0.338 · 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 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 routes1
Has abstractyes

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