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Record W4400593416 · doi:10.47743/ejes-2024-0111

Does women's empowerment improve women's education?A cross-sectional study of 27 transitional post-communist countries

2024· article· en· W4400593416 on OpenAlexaff
Alena Auchynnikava, Nazim Habibov, Yunhong Lyu

Bibliographic record

VenueEastern Journal of European Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsTrent UniversityUniversity of Windsor
Fundersnot available
KeywordsEmpowermentEducational attainmentUnit (ring theory)Asset (computer security)OddsDemographic economicsCommunismHigher educationSociologyEconomic growthLogistic regressionEconomicsPolitical sciencePsychologyPoliticsMedicine

Abstract

fetched live from OpenAlex

The paper examines the correlation between patriarchal attitudes, women’s asset ownership, participation in household decision-making and women's educational attainment across 27 post-communist countries. It hypothesizes that patriarchal attitudes hinder women's educational achievements while women's asset ownership and participation in household decision-making facilitate them. Utilizing regression analysis, marginal effects, post-regression simulation, the study tests and confirms these hypotheses. Results show that for every unit increase in women's asset ownership and participation in decision-making, the odds of achieving higher educational attainment increase by approximately 35.7% and 16.5%, respectively. Conversely, a unit increase in patriarchal attitudes decreases these odds by 15.8%. The findings underscore the importance of state and civil society commitment to addressing gender disparities in education.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.016
GPT teacher head0.322
Teacher spread0.306 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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