Comprehensive School Reform: Meta-Analytic Evidence of Black-White Achievement Gap Narrowing
Bibliographic record
Abstract
This meta-analysis extends a previous review of the achievement effects of comprehensive school reform (CSR) programs (Borman, Hewes, Overman, & Brown, 2003). That meta-analysis observed significant effects of well endowed and well-researched programs, but it did not account for race/ethnicity. This article synthesizes 34 cohort or quasi-experimental outcomes of studies that incorporated the policy-critical characteristic of race/ethnicity. FINDINGS: compared with matched traditional schools, the black-white achievement gap narrowed significantly more among students in CSR schools. In addition, the aggregate effects were large, substantially to completely eliminating the achievement gap between African American and non-Hispanic white students in elementary and middle schools. Title I policies before or after the No Child Left Behind Act of 2001 seem to have had essentially no impact on the black-white achievement gap. Curricular and testing mandates along with the threat of sanctions without concomitant resource supports seem to have failed. This study suggests that educational achievement inequities need not be America's destiny. It seems that they could be eliminated through concerted political will and ample resource commitments to evidence-based educational programs.
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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 | 0.047 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.023 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".