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Record W3090150410 · doi:10.1177/0020702020960120

Whose feminism(s)? Overseas partner organizations’ perceptions of Canada’s Feminist International Assistance Policy

2020· article· en· W3090150410 on OpenAlexafffundabout
Sheila Rao, Rebecca Tiessen

Bibliographic record

VenueInternational Journal Canada s Journal of Global Policy Analysis · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsUniversity of OttawaGlobal Affairs CanadaCarleton University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsFeminismEmpowermentGender studiesForeign policyPolitical scienceSociologyWomen's empowermentGender equalityFeminist philosophyEconomic growthPublic administrationPoliticsLawEconomics

Abstract

fetched live from OpenAlex

Canada's Feminist International Assistance Policy, introduced in 2017, is an ambitious and forward-thinking policy focussed on gender equality and women's empowerment. The emphasis on a feminist vision, however, raises questions about how feminism is defined and interpreted by Canada's partners in the Global South. In this article, we examine the interpretations of feminism(s) and a feminist foreign policy from the perspective of NGO staff members in East and Southern Africa. The research involved interviews with 45 Global South partner country NGO staff members in three countries (Kenya, Uganda, and Malawi). We consider the partner organization reflections on Canada's Feminist International Assistance Policy using a transnational feminist lens. Our findings provide insights into future considerations for Canada's feminist foreign policy priorities, consultations, and programme design.

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)0.008
metaresearch head score (Gemma)0.011
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 score0.094
Threshold uncertainty score0.681

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0310.019
Scholarly communication0.0150.003
Open science0.0010.005
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.015
GPT teacher head0.325
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

Citations20
Published2020
Admission routes3
Has abstractyes

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