Gender Inequity in Education Worldwide
Bibliographic record
Abstract
This thesis discusses gender inequity across twelve diverse countries, including Niger, Haiti, the Central African Republic, India, Afghanistan, Ecuador, Guatemala, China, the United States, Canada, the Netherlands, and Finland. The research aims to provide a comprehensive understanding of gender disparities by examining socio-cultural, economic, and political factors within various countries, emphasizing the importance of addressing gender disparities and promoting inclusivity globally. The thesis is organized into the following sections. First, a review of the literature provides context to the topic of gender inequity in education. This is followed by a discussion and overview of various sociological theories that contribute to our understanding of this subject matter. The third section explains the methodology designed to conduct the study. Subsequently, the results are presented, categorized by country. A thematic discussion of the findings follows to enhance our understanding of their importance. Finally, the thesis concludes with a discussion on the significance of the results and a call to action. The findings reveal varying degrees of gender inequity, with disparities evident in educational opportunities, workforce participation, political representation, and health outcomes. Common themes of gender inequity emerge across the countries, with socio-cultural barriers playing a significant role. Patriarchal attitudes, limited roles for women, and traditional views on gender roles contribute to early marriages, restricted autonomy, and gender-based violence. A call to action is presented to ensure that every girl and woman around the world has the opportunity and ability to reach their full academic potential.
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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.001 |
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.
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".