Understanding early childhood education and care utilization in Canada: implications for demand and oversight
Bibliographic record
Abstract
Abstract This study examined early childhood education and care (ECEC) utilization in Canada, focusing on use of unlicensed home child care (HCC) from an equity perspective. Data from the 2011 cycle of the General Social Survey (GSS) were used. Across Canada, parent responses reveal that 16.6% of children between the age of 12 months and entry to school were in unlicensed HCC. Another 24% of working parents reported having no regular form of non-parental childcare. Families with higher incomes were more likely to report using center-based care. Conversely, lower-income working parents with lower levels of education were more likely to use unlicensed HCC or report using no non-parental care at all. Comparison of parent responses in Ontario, however, where government estimates for the number of licensed and unlicensed HCC spaces are available, revealed that more parents report that their children are in licensed HCC than is possible. The lack of accurate parental reporting calls into question a key assumption of current regulatory systems, which is that parents are informed consumers of ECEC services. Given that many parents misreport the type of HCC their children use, and the equity concerns raised by the overall utilization patterns we found, we argue that governments need to take a more active role in oversight and support of HCC.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".