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Record W2313820225 · doi:10.1017/s1743923x11000092

Gender and State Architectures: The Impact of Governance Structures on Women's Politics

2011· article· en· W2313820225 on OpenAlexaff
Jill Vickers

Bibliographic record

VenuePolitics & Gender · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsCarleton University
Fundersnot available
KeywordsPolitical scienceScholarshipIdeologyPoliticsState (computer science)Corporate governancePower (physics)Unitary statePolitical economyOpportunity structuresGender studiesSociologyLawEconomics

Abstract

fetched live from OpenAlex

This essay explores gender scholarship about how state architectures affect women's politics. A rapidly growing literature addresses the effects of vertical and horizontal power divisions in federations. While just one in five states is a federation, federations govern 40% of the world's population (Watts 2008). Globalization and neoliberal ideology foster regionalization, devolution, and increased influence by international agencies, and so more people experience multilevel governance (MLG) in unitary states, too. Hence, feminist scholars now increasingly consider the impact of state architectures. The field's core idea is that while socioeconomic and ideological forces shape gender/power relations, governments and movements respond from within specific state architectures. Related debates involve how institutional designs shape opportunity structures, and how feminists respond to federal diversity.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.009
Scholarly communication0.0050.003
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.062
GPT teacher head0.342
Teacher spread0.280 · 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 designObservational
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

Citations17
Published2011
Admission routes1
Has abstractyes

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