Evaluating the Effectiveness of Education Aid in Promoting Inclusive and Equitable Quality Education with a Specific Emphasis on the Gender Perspective
Bibliographic record
Abstract
The primary objective of this study is to examine the relationship between education aid and different levels of schooling, specifically primary, secondary, and tertiary education, from a gender perspective, with a particular focus on Sustainable Development Goal (SDG) four. The study is structured into three main parts: the analysis of female outcomes, the analysis of male outcomes, and conducting a comparative analysis of results between females and males. Firstly, the study analyzes the impact of education aid on completion rates for females and males at the primary level, net enrolment rates for females and males at the secondary level, and gross enrolment rates for females and males at the tertiary level. Subsequently, a comparative analysis of the female and male outcomes is conducted. The study drew data from a 19-year panel (2002-2020) of fifty low and lower-middle-income countries. The system GMM (One-step GMM and Two-step GMM) was utilized for the analysis. Both methods demonstrated a favorable correlation between education aid and primary and secondary education. However, the results suggest that males benefit more from education aid than females at primary and secondary levels. Additionally, the findings for the tertiary level demonstrate that the relationship between tertiary education aid and tertiary education is not optimal. The primary contribution of this study lies in its focused examination of the impact of a specific level of educational aid on particular educational outcomes, with a special emphasis on gender considerations within a comprehensive framework aligned with SDG four.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".