Neighborhoods, Schools, and Academic Achievement: A Formal Mediation Analysis of Contextual Effects on Reading and Mathematics Abilities
Bibliographic record
Abstract
Although evidence indicates that neighborhoods affect educational outcomes, relatively little research has explored the mechanisms thought to mediate these effects. This study investigates whether school poverty mediates the effect of neighborhood context on academic achievement. Specifically, it uses longitudinal data from the Panel Study of Income Dynamics, counterfactual methods, and a value-added modeling strategy to estimate the total, natural direct, and natural indirect effects of exposure to an advantaged rather than disadvantaged neighborhood on reading and mathematics abilities during childhood and adolescence. Contrary to expectations, results indicate that school poverty is not a significant mediator of neighborhood effects during either developmental period. Although moving from a disadvantaged neighborhood to an advantaged neighborhood is estimated to substantially reduce subsequent exposure to school poverty and improve academic achievement, school poverty does not play an important mediating role because even the large differences in school composition linked to differences in neighborhood context appear to have no appreciable effect on achievement. An extensive battery of sensitivity analyses indicates that these results are highly robust to unobserved confounding, alternative model specifications, alternative measures of school context, and measurement error, which suggests that neighborhood effects on academic achievement are largely due to mediating factors unrelated to school poverty.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".