Essays on The Supply of Education and Student Achievement
Bibliographic record
Abstract
This dissertation examines the relationship between the supply of education and student achievement in three distinct contexts and across three levels of schooling. The first chapter, Private School Entry, Sorting, and the Performance of Public Schools: Evidence from Rural Pakistan, asks whether private school expansion in rural Punjab, Pakistan, affects the academic performance of students in primary public schools. I show that younger students from wealthier households and students who perform better in school are more likely to exit public schools and enroll in private schools. I conclude that sorting does not have negative consequences for the academic performance of students who stay in public schools. The second chapter, Searching for Answers: The Impact of Student Access to Wikipedia, evaluates an intervention from a randomized controlled trial in Malawi in which secondary school students got access to Wikipedia. Students searched for information related to their school syllabus, sexuality, African news, and a variety of topics that cater to their interests. We find that access to Wikipedia improved exam scores in English and Biology, especially among low achievers. The third chapter, What Sets College Divers and Thrivers Apart: A Contrast in Study Habits, Attitudes, and Mental Health, is an article published in 2019 in Economics Letters. We examine study habits and mental health among college students in Canada. Compared to students who do very well in college, students who perform poorly struggle on several dimensions: they feel more depressed, study less, and do not utilize the free university resources. The three chapters explore the common themes of student academic performance and school resources, from primary, secondary, to tertiary education, and across three continents. The findings inform policymakers about the role of the supply of education on student achievement.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".