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Record W4237527044 · doi:10.32920/ryerson.14644461

Violence against Afghan immigrant women in Canada: cultural influences in help-seeking behaviours

2021· preprint· en· W4237527044 on OpenAlexaffabout
Mushtari Ahmed

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicPolitics and Conflicts in Afghanistan, Pakistan, and Middle East
Canadian institutionsToronto Metropolitan UniversityOntario Tech University
Fundersnot available
KeywordsAfghanImmigrationPhenomenonDomestic violenceGender studiesCriminologySocial psychologyPsychologySociologyPolitical scienceSuicide preventionPoison controlMedicineEnvironmental health

Abstract

fetched live from OpenAlex

There have been studies on abuse against immigrant women, in spousal relationships. There is also literature on state violence against women in Afghanistan. Research to date has shown that there are various structural and cultural barriers affecting the help-seeking behaviours of many immigrant women subjected to spousal abuse. If Afghan culture is preserved in Canada, then, along with potential barriers that exist as immigrants, many of these women are constrained to seek help because of cultural barriers. This issue is important to address in order to make awareness of the phenomenon. My research question is: How does culture influence abused Afghan immigrant women's help-seeking behaviours in Canada? Interviews with service providers in the Afghan community were conducted to explore explanations for the victims’ behaviours. Much of the findings were in keeping with past research related to immigrant women's lives. However, this study is unique given the group in question. I conclude that patriarchal practices rather than cultural essentialism explain the phenomenon of violence and help-seeking behaviours.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.227

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0130.003
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.023
GPT teacher head0.288
Teacher spread0.265 · 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

Citations2
Published2021
Admission routes2
Has abstractyes

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