Gendered geographical inequalities in junior high school enrollment
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the spatial patterns of gender inequality in junior high school enrollment and the educational resource investments associated with the spatial trends. Design/methodology/approach The paper uses data on 170 districts in Ghana and hot spot analysis based on the Getis-Ord Gi statistic, linear regression, and geographically weighted regression to assess spatial variability in gender parity in junior high school enrollment and its association with resource allocation. Findings The results reveal rural-urban and north-south variability in gender parity. Results show that educational resources contribute to gender parity. At the national level, educational expenditure, and the number of classrooms, teachers, and available writing places have the strongest positive associations with girls’ enrollment. These relationships are spatially moderated, such that predominantly rural and Northern districts experience the most substantial benefits of educational investments. Practical implications The findings show that strategic allocation of infrastructure, financial, and human resources through local governments holds promise for a more impactful and sustainable educational development of all children, regardless of gender. Besides seeking solutions that address the lack of resources at the national level, there is a need for locally tailored efforts to remove the barriers to equitable distribution of educational resources across gender and socioeconomic groups. Originality/value This paper’s use of advanced spatial analysis techniques allows for in-depth examination of gender parity and investments in educational resources, and highlights the spatial nuances in how such investments predict gender disparities in junior high school enrollment. The findings speak to the need for targeted and localized efforts to address gender and geographical disparities in educational opportunities.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".