Looking Beyond the Household: The Importance of School Factors in Understanding Elementary Grade Retention in Rural Khyber Pakhtunkhwa, Pakistan
Bibliographic record
Abstract
This paper contributes to the limited literature on the educational outcomes of children in rural Khyber Pakhtunkhwa (KP), Pakistan. It explores the impact of school-level factors such as physical resources, teachers and school characteristics on retention to the last grade of primary in the KP province for the time period 2007-12. Two sources of data were used to measure the retention rates. One of which is an official compilation of institutional data on education known as Education Management Information System (EMIS). The second data source, Annual Status of Education Reports (ASER), is a household data set with a rich set of household covariates, teachers’ characteristics and student performance data on reading and mathematics. The results from regression analyses indicate that children are more likely to complete primary education cycle when they receive instructions in local language and when the pupil-teacher ratio is below a certain threshold. Results also reveal that a continuous increase in school size beyond a certain threshold (> 400 enrollment) is related to a decrease in retention rate. Further, mixed schools (all-boys’ schools having girls enrolled in them) were found to have better retention rates than boys’ schools.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".