Test Score Gaps in New York State Schools
Bibliographic record
Abstract
In this report, we analyze performance gaps by race/ethnicity, income and gender in New York State schools using fourth and eighth grade math and English language test results. While previous studies typically focus on gaps between subgroups at the district, state or national level, this study explores the school-level gaps in performance. Our results highlight the legacy of residential racial segregation – many schools have too few whites or non-whites to allow a meaningful calculation of the subgroup test performance or test score ‘gap ’ between groups within a school. Although our minimum subgroup size was six, which is relatively small, only 45.7 % of elementary schools were found to have enough whites and non-whites to calculate meaningful gaps. About one-third of elementary and middle schools are predominantly white and one fifth predominantly non-white. While roughly one quarter of all fourth graders and one fifth of all eighth graders attend predominantly white schools, twenty-three percent of fourth graders and fifteen percent of eighth graders attend predominantly non-white schools. Thus, integrated schools, disproportionately located in New York City and the downstate suburban districts, educate only about half of the students in the State. The implication is that the state-wide test score gaps significantly reflect both gaps between
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".