Do Neighbourhoods Influence the Readiness to Learn of Kindergarten Children in Vancouver? A Multilevel Analysis of Neighbourhood Effects
Bibliographic record
Abstract
A growing body of literature has examined the effects of neighbourhood characteristics on child health and well-being and the mechanisms through which such effects may operate. Research investigating neighbourhood effects on children is based on the notion that individuals and families who live in a neighbourhood collectively create a social context that influences the developing child. In this paper we investigate the relationship between individual and neighbourhood socioeconomic characteristics and kindergarten children's readiness to learn in Vancouver, Canada ( n = 3736), using multilevel modeling techniques and 1996 census data for Vancouver neighbourhoods ( n = 68). Findings suggest that although family-level characteristics carry the most weight in shaping children's readiness to learn, neighbourhood-level factors are independently associated with early developmental outcomes, particularly physical health and well-being, language and cognitive development, and communications skills and general knowledge. The strongest neighbourhood characteristics associated with readiness to learn were median income and the percentage of single-parent families. Also important were the percentage of the population who had not moved in the previous five years and the percentage of the population whose mother tongue was non-English. The latter neighbourhood characteristic was an especially strong predictor of communication skills and general knowledge. The findings suggest that neighbourhood-based policies to improve physical health and well-being, language and cognitive development, and communications skills may also meet with some success.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".