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Record W2117491599 · doi:10.1177/0886260504263868

Gender Inequality, Violence Against Women, and Fear

2004· article· en· W2117491599 on OpenAlexaff
Carrie Yodanis

Bibliographic record

VenueJournal of Interpersonal Violence · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSexual violenceDomestic violenceInequalityArgument (complex analysis)PsychologyGender inequalityPoison controlInjury preventionHuman factors and ergonomicsSocial psychologyCriminologyMedicineMedical emergency

Abstract

fetched live from OpenAlex

This article presents a cross-national test of the feminist theory of violence against women. Combining data from the International Crime Victims Survey (ICVS) with United Nations statistics, the findings support the theory. Specifically, the results indicate that the educational and occupational status of women in a country is related to the prevalence of sexual violence against women. In countries where the status of women is low, prevalence of sexual violence against women tends to be higher. In turn, sexual violence is related to higher levels of fear among women relative to men. In comparison, in countries where the status of women is high, sexual violence against women is lower. The findings of this study add confirmation to the argument that we need to look beyond individual level variables to understand and develop strategies for reducing violence against and fear among women.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
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 head0.031
GPT teacher head0.328
Teacher spread0.296 · 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 designObservational
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

Citations388
Published2004
Admission routes1
Has abstractyes

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