Child Care and Young Children: A Practitioner’s View
Bibliographic record
Abstract
The research findings of the CEECD papers1-8 confirm that demand for child care continues to escalate for all age groups. More and more mothers participate in the workforce at increasingly earlier points in their child’s life. However, the availability of regulated child care continues to lag far behind the supply. The development of new spaces has been painfully slow, inconsistent and uneven in Canada. For example, the Quebec government has taken the lead by significantly expanding spaces with the goal of universal accessibility for all children regardless of reason for service. The province of Manitoba has developed a Five Year Plan for Child Care (2002), designed to enhance three major elements: quality, accessibility and affordability. Child care appears to be on the upswing again in Ontario. In early 2004, the Ontario government announced it will spend $9.6M in federal child-care money to help cash-starved daycare centres make health and safety improvements. But some provinces, such as British Columbia, have experienced serious reductions in government funding at a time when the need is greater than ever and early childhood development is a hot topic. We are miles away from universal access, but the scope of the gap becomes more clear
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.026 | 0.028 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".