Residential neighborhood greenery and children's cognitive development
Bibliographic record
Abstract
Children who grow up in neighborhoods with more green vegetation show enhanced cognitive development in specific domains over short timespans. However, it is unknown if neighborhood greenery per se is uniquely predictive of children's overall cognitive development measured across many years. The E-Risk Longitudinal Study, a nationally representative 1994-5 birth-cohort of children in Britain (n = 1658 urban and suburban-dwelling participants), was used to test whether residential neighborhood greenery uniquely predicts children's cognitive development across childhood and adolescence. Greenery exposure was assessed from ages 5 to 18 using the satellite imagery-based normalized difference vegetation index (NDVI) in 1-mile buffers around the home. Fluid and crystalized intellectual performance was assessed in the home at ages 5, 12, and 18 using the Wechsler Intelligence Scale, and executive function, working memory, and attention ability were assessed in the home at age 18 using the Cambridge Neuropsychological Test Automated Battery. Children living in residences surrounded by more neighborhood greenery scored significantly higher, on average, on IQ measures at all ages. However, the association between greenery and cognitive measures did not hold after accounting for family or neighborhood socioeconomic status. After adjustment for study covariates, child greenery exposure was not a significant predictor of longitudinal increases in IQ across childhood and adolescence or of executive function, working memory, or attention ability at age 18. Children raised in greener neighborhoods exhibit better overall cognitive ability, but the association is likely accounted for by family and neighborhood socioeconomic factors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".