Does neighbourhood residence influence the readiness to learn of kindergarten children in Vanouver? : a multilevel analysis of neighbourhood effects
Bibliographic record
Abstract
This thesis investigates the relations between socio-economic dimensions of neighbourhoods and readiness to learn scores among kindergarten children, independent of family income. The study is based on readiness to learn data collected for 3,721 children attending kindergarten in the Vancouver School District in February 2000. Readiness to learn is assessed by each child's teacher using the Educational Development Instrument (EDI), a questionnaire that assesses readiness to learn in five sub-scales: emotional health and maturity, social knowledge and competence, communication skills and general knowledge, physical health and well-being, and language and cognitive development. Factor analysis at the census tract level is used to agglomerate Vancouver census tracts with similar socio-economic dimensions into 68 neighbourhoods that have a minimum of 30 kindergarten children. Map displays, correlation analysis, and regression analysis, at the ecological level, show a positive relationship between readiness to learn in each of the 5 sub-scales and neighbourhood socio-economic status. Multilevel analysis shows that the socioeconomic status of neighbourhoods has an independent effect on children's readiness to learn, when controlling for family income and ESL status, in each of the 5 EDI sub-scales. Results indicate that a neighbourhoods' socioeconomic status statistically accounts for under 3 percent of the variance in children's readiness to learn. Multilevel analysis for each of the sub-scales show that language and cognitive skills have stronger neighbourhood effects than emotional maturity and social skills, suggesting neighbourhoods may have more influence on certain dimensions of children's development.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 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".