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Record W4361195676 · doi:10.1080/23311886.2023.2194731

Openness of political structures and gender gaps in protest behaviour in Africa

2023· article· en· W4361195676 on OpenAlexaff
Eugene Emeka Dim

Bibliographic record

VenueCogent Social Sciences · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPoliticsOpenness to experienceContext (archaeology)Agency (philosophy)Political sciencePolitical economyPolitical opportunityPolitical communicationGender gapDevelopment economicsSociologySocial movementSocial psychologyDemographic economicsPsychologySocial scienceEconomicsLaw

Abstract

fetched live from OpenAlex

Protest behaviour has been conceptualized as a high-risk form of political engagement, and it tends to elicit a relatively lower engagement rate than other forms of political participation. In Africa, the risky nature of protests is often complicated by the predominant socio-cultural bias and masculine political norms that hinder women’s political agency. Many of these political systems in Africa are emerging democracies, where women are likely to be marginalized in the civic and political sphere. Using the Afrobarometer data of 2014/2015, this study seeks to examine the impact of the political context on the gender gap in protest behaviour. The study finds that the gender gap in protest behaviour is lower in countries that are politically free and higher in countries with more years of military regimes. These findings offer valuable insights into the political and institutional contexts in which women’s protest behaviour is accentuated and diminished.

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)0.007
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 score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.152
GPT teacher head0.418
Teacher spread0.267 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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