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Record W2475171680 · doi:10.1017/s1743923x16000477

Women's Transnational Activism, Norm Cascades, and Quota Adoption in The Developing World

2016· article· en· W2475171680 on OpenAlexaff
Liam Swiss, Kathleen M. Fallon

Bibliographic record

VenuePolitics & Gender · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsBeijingChinaNorm (philosophy)Political sciencePoliticsDeveloping countryDevelopment economicsEconomicsEconomic growth

Abstract

fetched live from OpenAlex

Electoral quotas are a key factor in increasing women's political representation in parliaments globally. Despite the strong effects of quotas, less attention has been paid to the factors that prompt countries to adopt electoral quotas across developing countries. This article employs event history modeling to analyze quota adoption in 134 developing countries from 1987 to 2012, focusing on quota type, transnational activism, and norm cascades. The article asks the following questions: (1) How might quota adoption differ according to quota type—nonparty versus party quotas? (2) How has the 1995 Fourth World Conference on Women in Beijing, China (Beijing 95), contributed to quota diffusion? (3) Do global, regional, or neighboring country effects contribute more to quota adoption? Results provide new evidence of how quota adoption processes differ according to quota type, the central role played by participation in Beijing 95, and how increased global counts contribute to faster nonparty quota adoption while increased neighboring country counts lead to faster to party quota adoption.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.078
GPT teacher head0.338
Teacher spread0.260 · 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

Citations38
Published2016
Admission routes1
Has abstractyes

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