School Improvement Grants in Ohio: Effects on Student Achievement and School Administration
Bibliographic record
Abstract
The federal School Improvement Grant (SIG) program allocated US$7 billion over nearly a decade in an effort to produce rapid and lasting improvements in schools identified as low performing. In this article, we use a regression discontinuity design to estimate the effect of Ohio’s SIG turnaround efforts on student achievement and school administration. The results indicate that Ohio’s SIG program significantly increased reading and math achievement, with effects in both subjects of up to 0.20 standard deviations in the second year after SIG eligibility identification. Estimates for the third year are somewhat larger, in the range of one quarter of a standard deviation. We provide evidence that these effects were primarily attributable to schools that implemented the SIG Turnaround model. We also show that SIG eligibility had a positive effect on per-pupil spending, but no average effect on administrative outcomes, including staff turnover, the number of staff members in the school, and school closure. These null overall effects mask heterogeneity across SIG models, however. Most notably, Turnaround schools experienced more turnover than they otherwise would have, whereas Transformation schools experienced less.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".