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Record W3088337726 · doi:10.1177/0020702020954547

Canada’s feminist foreign policy and human security compared

2020· article· en· W3088337726 on OpenAlexaffabout
Heather A. Smith, Tari Ajadi

Bibliographic record

VenueInternational Journal Canada s Journal of Global Policy Analysis · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsDalhousie UniversityUniversity of Northern British Columbia
Fundersnot available
KeywordsTransformative learningForeign policyRhetoricScholarshipCraftPolitical scienceSociologyArticulation (sociology)Human rightsGender studiesPolitical economyPublic administrationLawPolitics

Abstract

fetched live from OpenAlex

Canadian federal governments regularly try to craft a unique image of Canada in the world; however, the Trudeau government’s embrace of feminist foreign policy feels strikingly similar to the late 1990s when human security was embraced. There seems to be a “sameness” in the promotion of a progressive values-based discourse that has transformative potential for Canadian foreign policy. The question is, does this sense of sameness bear out when we dig into the comparison? Drawing on speeches given by government ministers; policy documents, such as the Feminist International Assistance Policy (FIAP); media; and scholarship, we compare and contrast analyses of the sources of the human security and feminist foreign policy discourses and then identify common critiques. We also examine two significant differences. We find there is consistent Liberal articulation of values-based discourses and policies that have unmet transformative potential. In both cases, style and rhetoric are privileged over transformative change.

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)0.003
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 score0.166
Threshold uncertainty score0.968

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.007
Science and technology studies0.0260.025
Scholarly communication0.0120.003
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.000

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 head0.025
GPT teacher head0.335
Teacher spread0.310 · 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

Citations18
Published2020
Admission routes2
Has abstractyes

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