Changes in Development Among Kindergarten Children in Ontario 2012-2015: Linking Developmental, Sociodemographic, and Policy Implementation Data
Bibliographic record
Abstract
IntroductionIn Ontario kindergarten children, measures of early child development (ECD) indicate worsening outcomes over the last two provincial measurements (2012-2015), particularly among indicators of early physical, emotional and social development. This is despite significant investments in early childhood in the province through the roll-out of universal full day kindergarten. Objectives and ApproachOur objective is to uncover correlates of change in the measures of ECD using the Early Development Instrument (EDI) between 2012 and 2015, particularly in relation to the average home/neighbourhood environments of students where school-level outcomes declined. This analysis links individual EDI data in Ontario, with 2016 DA-level Canadian Census data aggregated to the school level. The EDI is a kindergarten teacher-completed questionnaire measuring school readiness across five domains of child development. The schools are classified into groups based on whether they experienced statistically meaningful change in the domains of the EDI over the observed timeframe. ResultsThe changes observed at the provincial level were consistent with those observed at the school level. Developmental vulnerability increased overall, in the Physical Health and Well-being, Social Competence and Emotional Maturity domains. Vulnerability decreased in the Language and Cognitive Development and Communication Skills and General Knowledge domains. While the analyses are still ongoing, preliminary findings suggest that schools with a higher proportion of children from high immigrant, and high income neighbourhoods tended to improve more than other schools. These along with some other socioeconomic neighbourhood characteristics identify sub-groups of schools that tended to see more positive or negative change on average across the five domains. We will supplement these findings using the time of introduction of the free full-day kindergarten program in each school. Conclusion/ImplicationsThe presentation breaks down recent trends in kindergarten school readiness to identify schools that did better or worse than average based on socioeconomic and demographic characteristics. The analysis will also incorporate the timing of the introduction of full-day kindergarten into each school, providing insight into the effects of the program.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".