Listening to Care: Understanding the Impacts of Ontario’s Canada-Wide Early Learning and Child Care System
Bibliographic record
Abstract
In response to the growing issue of childcare affordability, the significant shortage of registered early childhood educators, and the lack of early learning childcare centre availability, the federal government implemented the Canada-Wide Early Learning and Child Care (CWELCC) system. The purpose of this research was to investigate the implementation decisions and process of opting-in to the CWELCC system in Ontario, and to explore the experiences of early childhood education and care (ECEC) operators as they navigate the CWELCC objectives of affordability, accessibility, inclusivity, quality, and flexibility. A mixed-methods questionnaire was sent to Ontario ECEC operators and data was collected from September-October 2023. The quantitative data included responses from 68 participants and the qualitative data included responses from 53 participants. Their perspectives and experiences were analyzed both inductively and deductively to examine if the system objectives were being met in Ontario. The analysis of the responses revealed the perceptions of Ontario ECEC operators regarding the implementation thus far. These perceptions included: the responsibility to families, supporting the workforce, the uncertainty of the agreement, the fear of future impacts, the concerns and challenges of navigating the system, the critiques of the implementation process, and the acknowledgment for potential positive outcomes of the system. Overall, participants shared that the objectives of the system were not being met in Ontario as the agreement proposed. The thesis concludes with implications, how the operators are navigating the implementation process, and future research recommendations.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.033 | 0.023 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".