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Record W2971478392 · doi:10.1080/14616742.2019.1607764

Trickle-down gender at the International Monetary Fund: the contradictions of “femina economica” in global capitalist governance

2019· article· en· W2971478392 on OpenAlexaff
Elaine Coburn

Bibliographic record

VenueInternational Feminist Journal of Politics · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsYork University
FundersWorld Bank Group
KeywordsGender mainstreamingCorporate governanceEmpowermentEconomicsPolitical sciencePoliticsPolitical economyEquity (law)SociologyEconomic growthGender studiesGender equalityFinanceLaw

Abstract

fetched live from OpenAlex

What forms do gender equity arguments take in major international institutions? What broader struggles do these reflect? What contradictions does gender mainstreaming reproduce and what feminist possibilities does it open up? This contribution describes, critiques, and tentatively explains the recent understudied “gender turn” in the International Monetary Fund (IMF) or the Fund, a major international financial institution. The IMF's concern with gender is late compared to other multilateral institutions, like the United Nations and the World Bank. Nonetheless, since 2013, the IMF has published more than 2,900 documents, videos, and podcasts specifically about women, alerting the public to gender equity matters through a dedicated “Gender and IMF” homepage and through Twitter, via the hashtags #Women4Growth and #IMFGender. In so doing, the IMF draws on existing, economistic “women's empowerment” initiatives by major finance and corporate enterprises, in newly opened policy space following the 2008–09 crisis, under unprecedented feminist leadership at the highest level of the Fund. The IMF's attentiveness to gender, pursued alongside market-led economic efficiency aims, produces a new actor, “femina economica.” This elastic figure is symptomatic of tensions within the organization and of broader contradictions in the current, uncertain moment of the capitalist world political economy.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.685
Threshold uncertainty score0.553

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.322
Teacher spread0.291 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations24
Published2019
Admission routes1
Has abstractyes

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