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Record W4405455595 · doi:10.1177/10778012241303475

“Him, Me, or the Both of Us”: Perceptions of Blame Among Israeli Ultraorthodox Women Survivors of Intimate Partner Violence

2024· article· en· W4405455595 on OpenAlexaff
Anat Vass, Julia Krane, Eli Buchbinder

Bibliographic record

VenueViolence Against Women · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsMcGill University
FundersHorizon 2020 Framework Programme
KeywordsBlameDomestic violenceAttributionPsychological interventionPsychologyPoison controlSuicide preventionSocial psychologyPerceptionHuman factors and ergonomicsMedicinePsychiatryMedical emergency

Abstract

fetched live from OpenAlex

This study investigated how Ultraorthodox Jewish Women (UJW) survivors attribute blame in intimate partner violence (IPV) experiences, addressing a significant research gap in understanding IPV within specific cultural contexts. The research employed a phenomenological approach through 15 semistructured face-to-face interviews with UJW IPV survivors. The findings revealed diverse patterns of blame attribution, including exclusive perpetrator blame, self-blame, and shared responsibility. The study suggests the need for a broader scientific framework to understand their experiences, emphasizing how IPV must be understood within both wider social and specific cultural contexts. This understanding is crucial for developing culturally sensitive interventions and support services for IPV survivors.

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)0.005
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.004
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.312
Teacher spread0.295 · 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
Published2024
Admission routes1
Has abstractyes

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