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Record W4285008940 · doi:10.1163/15718069-bja10063

Feminist Action at the Negotiation Table: An Exploration Inside the 2010–2016 Colombian Peace Talks

2022· article· en· W4285008940 on OpenAlexaff
Elizabeth S. Corredor

Bibliographic record

VenueInternational Negotiation · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsNegotiationPeacemakingGender mainstreamingPerspective (graphical)Gender studiesSociologyAction (physics)International relationsMainstreamingPolitical scienceGender equalityPoliticsLawSocial science

Abstract

fetched live from OpenAlex

Abstract Gender mainstreaming and peacemaking are fundamentally about spurring institutional change. Much of the literature on gendering peace negotiations does not explicitly address the institutional nature of these spheres. Using a feminist institutionalist framework, I analyze the 2010–2016 Colombian peace talks to uncover the endogenic formal and informal ‘rules of the game’ that both enabled and constrained feminist work and the eventual incorporation of a gender perspective within the final agreement. I show that Colombia’s exceptional gender perspective in its 2016 peace agreement was due not just to the inclusion of women at the negotiation table but also paradoxically because of and despite continued gendered logics that prioritized the masculine over the feminine. These findings demonstrate that to understand gender mainstreaming outcomes in peace processes we must not simply account for how many women and which women are at the table, but also for the gendered logics of the negotiation space.

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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.175

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0170.013
Scholarly communication0.0100.004
Open science0.0010.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0100.001

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 head0.082
GPT teacher head0.339
Teacher spread0.257 · 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

Citations16
Published2022
Admission routes1
Has abstractyes

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