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Record W2001459373 · doi:10.1163/187598412x639674

Mapping Gender and the Responsibility to Protect: Seeking Intersections, Finding Parallels

2012· article· en· W2001459373 on OpenAlexaff
Jennifer Bond, Laurel Sherret

Bibliographic record

VenueGlobal Responsibility to Protect · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Peace and Security Dynamics
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsResponsibility to protectParallelsStatus quoPolitical scienceHuman rightsIdentification (biology)Work (physics)Human securityMoral responsibilityInternational communityPublic relationsCollective responsibilityLawPoliticsEconomicsEngineering

Abstract

fetched live from OpenAlex

Over the past decade, the international community has acknowledged that traditional notions of conflict and protection must be re-visited if true human security is to be realized. Consistent with this recognition, both the responsibility to protect and the women, peace, and security agenda challenge the status quo and offer new perspectives from which to approach responses to conflict. Unfortunately, the former was developed without consideration of the latter, and a tremendous opportunity to benefit from years of experience and expertise was thus missed. This article demonstrates that while recent discourse surrounding the responsibility to protect suggests some increased awareness that conflict affects men and women differently, there remains a significant disconnect between the development of this framework and the ever-growing body of work on the gendered nature of peace and security issues. Our identification of this ongoing chasm is accompanied by two simple observations: first, that this renders the responsibility to protect inconsistent with other international commitments and priorities; and second, that incorporation of the links between gender and conflict will improve the ability of the responsibility to protect to afford true protection.

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)0.016
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.396
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.044
GPT teacher head0.346
Teacher spread0.302 · 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.

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

Citations1
Published2012
Admission routes1
Has abstractyes

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