Top-decile elementary scores from non-elite schools: 5 case studies
A reproducibility log: the data this analysis touched, the queries it ran, the outside sources it leaned on, and the steps a third party would follow to re-run it.
Summary
Filtered schools at or near the proficiency ceiling (academic composite ≥ 90% percentile) that are NOT in the city's elite-screened set (Anderson, TAG, NEST+m). Profiled five.
Data sources
- Database tableschools
One row per New York City public school (DBN, name, district, borough, school_type, grade_band, latest_enrollment, proclivity_decile, proclivity_score, closed_at, admission_category).
- Database tableschool_year_metrics
Long-format per-school per-year metric facts (school_dbn, year, metric_key, subgroup, value, suppressed). Loaded from DOE/NYSED public files via scripts/loaders/*.
Steps
Filter by academic composite ≥ 90 AND admission_category != 'SCREENED' AND not in the K-8 G&T set.
Caveats
'Non-elite' is a heuristic; some 'unscreened' schools have effective screens (sibling priority, zoning that selects on income).
Reproduce
Clone the repo, set DATABASE_URL to a Postgres with the project schema loaded, run `npx tsx scripts/loaders/<source>.ts` for any not-yet-loaded data, then issue the queries in the Steps section. The story page also lists the exact `metric_key`/`subgroup`/`year` filters used. Searchable by the answer's headline number — every figure is recomputable from the queries shown.
The recipe lives at data/stories/recipes.ts in the repo. Corrections welcome.