Do Targeted Stipend Programs Reduce Gender and Socioeconomic Inequalities in Schooling Attainment? Insights From Rural Bangladesh
Bibliographic record
Abstract
Social investment in schooling in low-income countries has increased greatly in the 1990s and 2000s because of the robust associations among schooling and demographic, economic, and health outcomes. This analysis investigates whether targeted school-attendance stipend programs succeeded in reducing gender and socioeconomic inequalities in school attainment among a sample of the rural poor in Bangladesh. Multivariate analyses find that targeted stipend programs helped to reduce the gender attainment gap. Females had an increased probability of participating in stipend programs, and returns to stipend participation were significantly higher for females. However, stipend programs failed to reduce the relative achievement gap between children of different socioeconomic backgrounds: low socioeconomic status (SES) was associated with a decreased probability of stipend participation, and stipend-related schooling gains for lower-SES females were matched by comparable gains for higher-SES females. Meanwhile, there was no significant association between stipend participation and schooling attainment for males.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".