Spatial Mismatch and the Share of Black, Hispanic, and White Students Enrolled in Charter Schools
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
How are patterns of segregation related to families’ engagement in public-school choice policies across U.S. metropolitan areas? This article examines how segregation in urban public schools and the spatial mismatch between school-age children and relatively high-performing schools relate to the shares of Black, Hispanic, and White students enrolled in charter schools, one particular school choice mechanism. Drawing on Core-Based Statistical Area–level data, I find that charter-school enrollment among Black students is positively associated with spatial mismatch. As the degree of geographic imbalance between Black and, to a lesser extent, Hispanic school-age children and high-performing schools increases, so too does the share of Black and Hispanic students who enroll in charter schools. There is no such relationship for White students, whose enrollment in charter schools is higher when school segregation is relatively low—that is, when they would be more likely to attend neighborhood public schools with Black children.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 | 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 it