What Can We Learn from Quebec's Universal Childcare Program?
Bibliographic record
Abstract
Childcare policy was an important issue in the recent election campaign. The parties ’ various childcare proposals all took account of the experience of Quebec’s universal childcare system. They differed sharply, however, on whether Quebec’s model should be emulated, as in the Liberal and NDP platforms, or avoided, as with the Conservatives ’ recommendation that childcare funding should go directly to parents, who might choose at-home care. All families in Quebec have access to provincially subsidized childcare, at an out of pocket cost of $7 per day. While there have been bumps along the way — queues for access and costly labour problems, for example — the system is very popular. We analyzed the impact of Quebec’s program on work choices, family functioning and children’s well-being and found some positive and some strikingly negative outcomes.1 What is best for children and parents? While we do not presume to provide the answer, our work does offer some fresh evidence from the childcare front for parents and policymakers to consider. What can the data tell us? Our study is based on data drawn from the National Longitudinal Study of Children and Youth.2 Our national sample of over 33,000 children covers newborns to 4-year-olds during the years from 1994 to 2002. The survey contains information on childcare use, parental labour market behaviour, and children and family health and behavioural measures. We compare the outcomes for children in Quebec to those of children in other parts of Canada, who act as a control group against whom to evaluate what we see in Quebec. We compare Quebec and the rest of
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".