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Record W4210549556 · doi:10.47389/37.1.65

Men’s role in violence against women in disasters: studies in Iran and Australia

2022· article· en· W4210549556 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAustralian Journal of Emergency Management · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsHealth Sciences North
Fundersnot available
KeywordsParallelsContext (archaeology)Project commissioningDomestic violenceCriminologyInequalityGender inequalityPublishingSuicide preventionPoison controlSociologyGender studiesPolitical scienceGeographyMedicineEngineeringEnvironmental healthLaw

Abstract

fetched live from OpenAlex

Sexual violence is largely absent from studies on violence against women in disasters. The role of men in perpetrating violence against women is overlooked or excused and women are usually blamed in both countries. A review of 2 studies of men’s violence against women after floods and earthquakes in Iran and bushfires in Australia show remarkable similarities. Although cultural contexts and the way gender inequality is established and demonstrated are different, these studies reveal unexpected parallels. The context of disaster lays it bare. Participants of both studies were disaster-affected people in Iran and Australia who revealed the taboos that prevent women speaking of violence that is exacerbated in a disaster context. Men play important roles in preventing and responding to violence against women as the result of their responsibilities and positions at the household and community levels. The objective of this paper was to compare the findings from these studies and consider the difficulties faced in conducting studies related to the roles of men and women roles during and after disaster events.

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.

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)0.003
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.409
Threshold uncertainty score0.610

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.101
GPT teacher head0.391
Teacher spread0.290 · 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