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Record W4200274082 · doi:10.1111/lasr.12576

Women's law-making and contestations of “marriage” in African conflict situations

2021· article· en· W4200274082 on OpenAlexafffund
Annie Bunting, Heather Tasker, Emily Lockhart

Bibliographic record

VenueLaw & Society Review · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsYork University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSierra leoneHarmLawJurisprudenceForced marriageCriminologyContext (archaeology)Crimes against humanityPolitical scienceInternational lawWar crimePoliticsAgency (philosophy)Criminal lawSexual violenceSociologySocial science

Abstract

fetched live from OpenAlex

Abstract International criminal law has developed significantly over the past 20 years since the establishment of the ad hoc Tribunals and International Criminal Court. Much scholarly attention has focused on the politics and jurisprudence of these courts, with particular focus on the prosecution of sexual and gender-based violence. This article adds to the literature with comparative, qualitative research with survivors of conflict-related forced marriage in Liberia, Sierra Leone, and Uganda, revealing context-specific understandings of marriage, consent and harm. We argue women exercise “tactic agency” in captivity in ways that are, taken together, “law-making” in their contestations over the socio-legal categories of marriage. Their contestations of marriage impact the norms within rebel groups as well as the development of new crimes against humanity in international criminal law. Building on the empirical findings, we argue that prosecution of crimes against humanity and reparation programs ought to be flexible and responsive enough to capture the varied experiences of women and girls abducted in war for purposes of sexual exploitation.

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)0.005
metaresearch head score (Gemma)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0010.003
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.060
GPT teacher head0.346
Teacher spread0.286 · 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

Citations8
Published2021
Admission routes2
Has abstractyes

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