How neighborhoods matter for rural and urban children's language and cognitive development at kindergarten and Grade 4
Bibliographic record
Abstract
Abstract The authors took a population‐based approach to testing how commonly studied neighborhood socioeconomic conditions are associated with the language and cognitive outcomes of residentially stable rural and urban children tracked from kindergarten (ages 5–6) to Grade 4 (ages 9–10). Child‐level kindergarten Early Development Instrument (EDI) data were probabilistically linked to scores on Grade 4's Foundation Skills Assessment (FSA), 4 years later, and to socioeconomic data describing the children's residential neighborhoods. Multilevel analyses were performed for a study population of 5,022 children residing in 105 neighborhoods across British Columbia, Canada: 635 children in 20 rural neighborhoods and 4,825 children in 85 urban neighborhoods. Concentrated immigration consistently predicted better child outcomes. Moreover, the determinants of children's language and cognitive outcomes analyzed cross‐sectionally differed from the determinants of outcomes analyzed longitudinally. Furthermore, there were notable differences in the extent of the relationship between neighborhood socioeconomic conditions and rural and urban children's outcomes over time. © 2010 Wiley Periodicals, Inc.
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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 | 0.002 |
| 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 | 0.000 |
| 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".