Bibliographic record
Abstract
This thesis contains three papers examining the effects of various policy choices, related to policing crime and providing schooling options for children, on the education outcomes of children in primary school. In Chapter 1, I examine the effects of a geographically targeted proactive policing strategy in Los Angeles on the education outcomes of young students. I document substantial heterogeneity in the effects of the policy, which enhanced police authority to arrest suspected gang members inside `safety zones'. Although the targeted policing strategy was effective in reducing crime and improving academic progress for many, the analysis draws attention to collateral damage inflicted on some at-risk children that ongoing and future policing initiatives could seek to mitigate. In Chapter 2, I investigate how the consolidations of public schools in an underperforming system impacted students and teachers who were displaced by closures, as well as those who were indirectly affected. Using a matched event study design that controls for students’ enrollment histories, we observe no persistent negative effects on students’ achievement in standardized tests, consistent with the existing literature. Moreover, students displaced from underperforming schools experience large achievement gains relative to their non-displaced peers. Altogether, the results indicate that cost-saving consolidations can be made without harm to achievement when schools are closed in the vicinity of adequate alternatives, and under ideal circumstances can be advantageous. Chapter 3 assesses whether expansion of school choice through the open enrollment provision of `No Child Left Behind' (NCLB) was effective in shifting students away from low performing schools in California. Using the discontinuous assignment to open enrollment under NCLB, I identify the causal effect of being sanctioned with open enrollment on subsequent enrollment growth. I uncover small and statistically insignificant enrollment declines for schools that marginally failed to make AYP and were sanctioned with school choice for one year, and statistically significant declines in enrolment growth of 2.7 percentage points for schools that had school choice for a second year. Heterogeneity analysis suggest that school supply constrains hinder the ability of school choices policies to improve education quality.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".