EARLY LEARNING AND CHILD CARE FOR IMMIGRANT FAMILIES AND CHILDREN: A PAN-CANADIAN JURISDICTIONAL SCAN OF SETTLEMENT AGENCIES
Bibliographic record
Abstract
High-quality early learning and child care programs are in a position to support immigrant family settlement, reduce socioeconomic inequities, and enhance children’s overall development. In Canada, these can be delivered either as provincially or territorially licensed programs or through settlement agencies. The goal of this research was to understand what factors influence the implementation of child care in settlement agencies across Canada. We conducted an environmental scan of settlement agencies and invited key informants to participate in interviews and surveys. Overall, the 38 participating organizations identified factors influencing the successful implementation of child care delivery at settlement agencies at both the system level (licensing and regulation, funding, workforce changes) and operational level (enhanced access to child care, cultural and linguistic diversity of educators). The findings also suggest a need to continue to emphasize broader purposes for early learning and child care programs, such as providing support to the whole family by allowing parents to access other services such as language training and information classes. Strategic connections between settlement agencies and provincially or territorially licensed programs will contribute to the professionalization of the field and to greater access to child care for immigrant families across the country.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".