Current and Emerging Issues in Gender Equality in Education: What Does the Data Tell Us?
Bibliographic record
Abstract
Abstract This chapter identifies current and emerging issues related to gender equality in education, providing a comparative analysis of education-related gender equality indicators across Kazakhstan, Kyrgyzstan, Tajikistan, and Uzbekistan. Using data from UNESCO, the World Bank Gender Data Portal, the United Nations Development Programme’s Gender Inequality Index, country-level data, and other sources, the chapter is organized into four sections. First, the gendered educational landscape is examined, encompassing educational participation and attainment in compulsory and post-compulsory education across the countries. Second, the chapter explores gender differences in fields of study in higher education to understand patterns of gendered behaviour in education. Third, the chapter examines the role of female educators—teachers and researchers, and fourth, the chapter explores whether educational achievements for women are translating into societal leadership gains. The chapter concludes with brief country summaries highlighting key issues for gender equality in education. Despite presenting some statistics for the first time, caution is warranted due to the subjective nature of data and its limitations in capturing root causes and structural effects of gender inequality.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".