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Record W4225303615 · doi:10.24124/2022/59263

Intersections of immigration and violence against women in relationships: A case study of the lived experiences of West African immigrant women in Northern British Columbia

2022· dissertation· en· W4225303615 on OpenAlexaboutno aff
Chibuzo Stephanie Okigbo

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationGender studiesTransformative learningInvisibilitySociologyViewpointsIntersectionalityVariety (cybernetics)Political sciencePedagogyLaw

Abstract

fetched live from OpenAlex

This research is for the researcher’s graduate thesis, as a requirement to obtain a Master of Social Work degree. The thesis research was conducted via distance (using telephone and password-protected audio conferencing) with four immigrant women. Using a case study approach and framed by socialist feminist intersectional theories and transformative framework, this thesis examines the intersections of immigration and violence against women in relationships (VAWIR) on multiple axis including gender, race, class, immigrant, and economic status to better understand factors that shape the experiences of visible minority immigrant women dealing with domestic violence and abuse. The findings of this thesis would contribute to different viewpoints on the experiences of VAWIR among immigrant women and bring more understanding to a variety of ways immigrant women respond to and cope with violence in their relationships as well as shape future policies and practices to more effectively service immigrant women of African descent.

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)0.002
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 score0.589
Threshold uncertainty score0.816

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0270.010
Scholarly communication0.0050.002
Open science0.0020.007
Research integrity0.0020.003
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 head0.013
GPT teacher head0.267
Teacher spread0.254 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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