Bibliographic record
Abstract
Formal programs for the care and education of young children in Canada have a history that goes back almost 200 years, yet issues surrounding services for our youngest Canadians continue to be hotly contested as we begin a new century. In Canada, early childhood care and education are striking for their tremendous diversity on such key issues as curriculum, financing, and teacher education. The range of programs and philosophies can be overwhelming for parents, practitioners, academics, researchers, and policy makers alike. Larry Prochner and Nina Howe reflect the variation within the field by bringing together a multidisciplinary group of experts to address key issues in the field: What programs are currently available and what are their origins? How are adults prepared for work in these programs? How do children within the programs spend their day? What policies guide the programs? How has the field reflected on itself through research? There are no simple answers, but the essays in this collection contribute to a creative reframing of the questions. The authors include psychologists, sociologists, historians, teacher educators, and social policy analysts. Early Childhood Care and Education in Canada will be of interest to students, teachers, and researchers in child study, early education, policy studies, and history. With cutbacks to early education programs, a shortage of daycare spaces, and uncertainty about future levels of support, the time is ripe for a close examination of the services we provide for our youngest citizens.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.032 | 0.009 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".