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Record W4414041180 · doi:10.1093/epolic/eiaf010

Gender and armed conflict

2025· article· en· W4414041180 on OpenAlexaff
Siwan Anderson, María Micaela Sviatschi

Bibliographic record

VenueEconomic Policy · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsUniversity of British Columbia
FundersForeign, Commonwealth and Development Office
KeywordsArmed conflictPoliticsResistance (ecology)Empirical researchEmpirical evidence

Abstract

fetched live from OpenAlex

Abstract This article synthesizes the quantitative literature on the relationship between gender and armed conflict, focusing on two key perspectives: women as victims and women as agents. It first reviews research on the consequences of conflict for women and girls, highlighting both direct effects—such as gender-based violence and forced displacement—and indirect effects in post-conflict settings, including declines in personal security, education, economic outcomes, and health. The article then shifts to examine women’s political roles in conflict, whether as participants in resistance movements or as peace-builders and agents of post-conflict reconstruction. While there is a growing body of innovative research on these critical issues, significant opportunities remain for further empirical and experimental work, particularly studies that seek to establish causal mechanisms and expand our understanding of gendered dynamics in settings damaged by conflict.

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)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.005
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.000

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 head0.039
GPT teacher head0.347
Teacher spread0.308 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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