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Record W173368851 · doi:10.22329/wyaj.v27i2.4532

Feminist Debates on Civilian Women and International Humanitarian Law

2009· article· en· W173368851 on OpenAlexaffvenue
Valerie Oosterveld

Bibliographic record

VenueWindsor Yearbook of Access to Justice · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsWestern University
Fundersnot available
KeywordsInternational humanitarian lawMainstreamPolitical scienceLawArmed conflictInequalityAction (physics)EnforcementCriminologyInternational lawSociology

Abstract

fetched live from OpenAlex

International humanitarian law [IHL] provisions address the situation of civilian women caught in armed conflict today, but is this law enough? Feminist commentators have considered this question and have come to differing conclusions. This article considers the resulting debate as to whether female-specific IHL provisions are adequate but underenforced, or inadequate, outdated and in need of revision. One school of thought argues that the main impediment to the protection of female civilians during hostilities is lack of observance of existing IHL. A second school of thought believes that something more fundamental is needed to meet the goal of protecting civilian women during war: revision and reconceptualization of IHL to take into account systematic gender inequality. This article considers the status of this debate within three areas of IHL considered by many to be central legal aspects of the experience of female civilians caught in armed conflict: the general non-discrimination provisions, the specific protection for civilian women against sexual violence and the specific protection of pregnant women and mothers. It concludes that, while there has been a vibrant debate within feminist circles on the adequacy of existing IHL provisions, mainstream action has tended to focus on enforcement. This is unfortunate, as it means that certain insights into the impact of deep gender inequalities on conflict have largely been left unexplored.

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 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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.815
Threshold uncertainty score0.412

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.052
GPT teacher head0.345
Teacher spread0.293 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations4
Published2009
Admission routes2
Has abstractyes

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