EDUCATIONAL ATTAINMENT AND LABOR FORCE PARTICIPATION AMONG SC/ST WOMEN IN RURAL INDIA
Bibliographic record
Abstract
This research examines the relationship between educational attainment and labor force participation among Scheduled Caste (SC) and Scheduled Tribe (ST) women in rural India.Despite constitutional protections and decades of affirmative action policies, SC and ST women remain disproportionately disadvantaged in both education and employment outcomes.Using secondary data from the Periodic Labour Force Survey (PLFS) 2023-24, Census 2011, and UDISE+ 2023-24, this study quantifies the intersectional barriers faced by these marginalized groups.The paper presents empirical data showing that rural SC women have a literacy rate of 63 percent compared to 73 percent among other rural women, while ST women face similar gaps in educational attainment.Paradoxically, ST women demonstrate higher labor force participation rates (34 percent) than other rural women (25 percent), yet this participation is concentrated in low-quality casual wage work.The paper employs logistic regression models with interaction terms to demonstrate that educational returns remain significantly lower for SC/ST women than for other groups.Mathematical models incorporating caste -education interaction effects reveal persistent employment segmentation.The research concludes that advancing SC/ST women's labor market outcomes requires simultaneous interventions in education quality, labor demand creation, anti-discrimination enforcement, and social norm transformation, rather than education-only approaches.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".