Understanding Disparities: An Analysis of Reasons for School Non-Attendance in India by Gender and Residence
Bibliographic record
Abstract
Abstract: School non-attendance creates a significant challenge to the educational sector in India. The disparities in non-attendance can be witnessed across gender and geographical locations. This research examines the reasons for school non-attendance, focusing on the differences between boys and girls as well as rural versus urban regions. By using a comprehensive dataset from the National Family Health Survey (NFHS), the study employs statistical analyses specially the chi-square test to analyze key factors influencing non-attendance, including socio-economic status, cultural norms, infrastructural deficiencies and so on. The findings reveal distinct patterns of non-attendance, with girls and rural children facing unique barriers that hinder their educational participation. The study provides policy recommendations aimed at addressing these barriers through targeted interventions, community engagement and improved educational infrastructure. By focusing on the critical issues affecting school attendance, this research aims to contribute to the development of more equitable and inclusive educational policies in India. Keywords: School Non-Attendance, Educational Disparities, Gender Differences, Rural Education, Urban Education, Socio-Economic Factors
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".