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Record W639333251 · doi:10.59962/9780774852050

Early Childhood Care and Education in Canada

2007· book· en· W639333251 on OpenAlexaboutno aff
Larry Prochner

Bibliographic record

VenueUniversity of British Columbia Press eBooks · 2007
Typebook
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsEarly childhood educationEarly childhoodPolitical sciencePsychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.467
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.158
Teacher spread0.153 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations33
Published2007
Admission routes1
Has abstractyes

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