Neighborhood Social Capital, Neighborhood Disadvantage, and Change of Neighborhood as Predictors of School Readiness
Bibliographic record
Abstract
Neighborhood income and social capital are considered important for child development, but social capital has rarely been measured directly at an aggregate level. We used Canadian data to derive measures of social capital from aggregated parental judgments of neighborhood collective efficacy and neighborhood safety. Measures of neighborhood income came from Census data. Direct measures of preschoolers’ school readiness were predicted from neighborhood-level variables, with regional indicators and household/parental characteristics taken into account. Our findings show that (1) residing in Quebec, being Black, and having a parent who was born outside Canada are positively associated with children’s living in disadvantaged or low collective efficacy neighborhoods as well as with their living in low-income households. (2) Children’s odds of residential mobility were reduced when the origin neighborhood had higher collective efficacy but increased when the family rented rather than owned. (3) Both neighborhood collective efficacy and children’s ever having lived in a poor neighborhood were correlated with receptive vocabulary scores, but results were mixed for other cognitive dimensions. Children of younger mothers scored worse on receptive vocabulary. There were similar patterns for demographic predictors related to visible minority status, sibship size, and birth order. Neighborhood average income had no effect on cognitive outcomes when the region was controlled.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".