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Record W3044364369 · doi:10.32920/ryerson.14639844.v1

Domestic Violence in Immigrant Communities : Case Studies

2021· preprint· en· W3044364369 on OpenAlexaboutno aff
Ferzana Chaze, Bethany Osborne, Archana Medhekar, Purnima George, Katrina Chahal

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicInternational Human Rights and Reproductive Law
Canadian institutionsnot available
Fundersnot available
KeywordsDomestic violenceImmigrationCriminologyPsychological interventionVulnerability (computing)Social workSociologyPolitical scienceGender studiesPoison controlSuicide preventionLawMedicineComputer securityNursing

Abstract

fetched live from OpenAlex

Domestic Violence in Immigrant Communities: Case Studies” is a freely accessible eCampus Ontario Pressbook containing case studies of immigrant women experiencing domestic violence to be used as educational materials. The contents were created by analysing closed legal case files of 15 immigrant women living in Ontario who experienced domestic violence. The comprehensive case studies that emerge from this research present domestic violence experienced by immigrant women in all its complexity, highlighting their unique vulnerability at the intersections of race, gender and immigration status. The book also highlights the different legal processes that these women encounter in seeking justice and the challenges they face in relation to re-establishing their own lives and the lives of their children. In addition to the cases, the book contains questions for reflection; a description of legal processes involved in DV cases, and a glossary of the terms used throughout the case studies. This interactive Pressbook is an ideal resource for social work and legal practitioners, including students in social service work, social work and law programs, in order to increase their understanding about the complexity of domestic violence cases in immigrant families and develop strategies for culturally informed interventions.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.466
Threshold uncertainty score0.848

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.079
GPT teacher head0.402
Teacher spread0.323 · 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.

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
Published2021
Admission routes1
Has abstractyes

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