Neighbourhood socioeconomic status indices and early childhood development
Bibliographic record
Abstract
The developmental health of young children is highly influenced by the socioeconomic conditions in which they are raised. How to accurately measure these conditions is a point of debate in the current literature on child development, health, and social determinants. We have evaluated four existing indices of socioeconomic status (SES) to determine the most relevant for the analysis of early childhood development (ECD) in Canada. Following a literature review of published SES indices which used 2006 Canadian Census data, four indices were chosen based on their relevance to ECD and the number of citations in subsequent articles. These were: the Canadian Deprivation Index, the Socioeconomic Factor Index, the Canadian Marginalization Index and an index created by the Early Childhood Mapping Project in Alberta, Canada. The indices were replicated using SES data for 2038 customized geographic neighbourhoods encompassing 99.9% of the Canadian population, and the relationship of the indices to ECD was investigated by linking to aggregated data from the Early Development Instrument (EDI), a teacher-completed questionnaire used to assess kindergarten children's physical, social, emotional, and cognitive development, and communication skills. The derived SES indices were compared based on four criteria: the input variables used, the index structure, the interpretability of the index and the variance they explained (R2) in the different EDI outcome measures. In terms of variance explained, material components of the SES indices (e.g., income, education) consistently showed the strongest association with children's language and cognitive development. The patterns of association for the non-material SES components and the other developmental domains of the EDI were more complex. We discuss the findings in regard to current developments in the field, and the need for refining empirical and theoretical approaches to examine associations between different facets of SES contextual factors and different aspects of ECD outcomes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| 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".