Bibliographic record
Abstract
Prompted by widespread concerns about public school quality, a growing empirical literature has measured the effects of greater choice on school performance.This paper contributes to that literature in three ways.First, it makes the observation that the overall effect of greater choice, which has been the focus of prior research, can be decomposed into demand and supply components: knowing the relative sizes of the two is very relevant for policy.Second, using rich data from a large metropolitan area, it provides a direct and intuitive measure of the competition each school faces.This takes the form of a school-specific elasticity that measures the extent to which reductions in school quality would lead to reductions in demand.Third, the paper provides evidence that these elasticity measures are strongly related to school performance: a one-standard deviation increase in the competitiveness of a school's local environment within the Bay Area leads to a 0.15 standard deviation increase in average test scores.This positive correlation is robust and is consistent with strong supply responsiveness on the part of public schools, of relevance to the broader school choice debate.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 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".