Modeling the Determinants of Out-of-School Children in Pakistan
Bibliographic record
Abstract
It is well known that different socioeconomic, household sizes, school levels, and cultural factors are highly related to the problem of out-of-school children. The main objective of this study is to quantify the determinants of out-of-school children in Pakistan. For this study, the data are used from the Pakistan Social and Living Standards Measurement (PSLM) survey, collected for the year 2015–16. The children aged 6–16 years who are in or out of school are considered as a unit of the analysis. Unlike the previous studies that generally provide a theoretical framework or empirical investigation based on simple econometric models, this work used fixed and random effect models capable of accounting for the hierarchical structure present in the data. To assess the results’ reliability, the models’ predictive ability has also been tested and found satisfactory. The results reveal many important aspects of the current problem regarding disparities between gender, provinces, regions, and other variables used in the analysis. For example, parental education is highly significant and has a negative impact on out-of-school children at the province and region level for both boys and girls. In addition, income levels, child labor, household size, and residency are highly influential on the problem of out-of-school children. Therefore, the government should prioritize female education, especially in rural areas. The provision of monetary incentives and enforcement of a strict ban on child labor can reduce the problem under study.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".