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Record W2903555282 · doi:10.1177/0047117818811892

Theorising feminist foreign policy

2018· article· en· W2903555282 on OpenAlexaboutno aff
Karin Aggestam, Annika Bergman Rosamond, Annica Kronsell

Bibliographic record

VenueInternational Relations · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsnot available
FundersRiksbankens Jubileumsfond
KeywordsForeign policyFeminist philosophyOntologyRelevance (law)Feminist theoryFeminist ethicsSociologyFeminismGender studiesPolitical scienceEpistemologyLawPolitics

Abstract

fetched live from OpenAlex

A growing number of states including Canada, Norway and Sweden have adopted gender and feminist-informed approaches to their foreign and security policies. The overarching aim of this article is to advance a theoretical framework that can enable a thoroughgoing study of these developments. Through a feminist lens, we theorise feminist foreign policy arguing that it is, to all intents and purposes, ethical and argue that existing studies of ethical foreign policy and international conduct are by and large gender-blind. We draw upon feminist International Relations (IR) theory and the ethics of care to theorise feminist foreign policy and to advance an ethical framework that builds on a relational ontology, which embraces the stories and lived experiences of women and other marginalised groups at the receiving end of foreign policy conduct. By way of conclusion, the article highlights the novel features of the emergent framework and investigates in what ways it might be useful for future analyses of feminist foreign policy. Moreover, we discuss its potential to generate new forms of theoretical insight, empirical knowledge and policy relevance for the refinement of feminist foreign policy practice.

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)0.009
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.015
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0070.051
Scholarly communication0.0070.007
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.036
GPT teacher head0.374
Teacher spread0.339 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations128
Published2018
Admission routes1
Has abstractyes

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