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Record W2982177430 · doi:10.1186/s40723-019-0063-8

Understanding early childhood education and care utilization in Canada: implications for demand and oversight

2019· article· en· W2982177430 on OpenAlexaffabout
Petr Varmuza, Michal Perlman, Linda A. White

Bibliographic record

VenueInternational journal of child care and education policy/International journal of child care and education · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEquity (law)Government (linguistics)Early childhood educationEarly childhoodPerspective (graphical)PsychologyPolitical scienceBusinessDemographic economicsDevelopmental psychologyEconomics

Abstract

fetched live from OpenAlex

Abstract This study examined early childhood education and care (ECEC) utilization in Canada, focusing on use of unlicensed home child care (HCC) from an equity perspective. Data from the 2011 cycle of the General Social Survey (GSS) were used. Across Canada, parent responses reveal that 16.6% of children between the age of 12 months and entry to school were in unlicensed HCC. Another 24% of working parents reported having no regular form of non-parental childcare. Families with higher incomes were more likely to report using center-based care. Conversely, lower-income working parents with lower levels of education were more likely to use unlicensed HCC or report using no non-parental care at all. Comparison of parent responses in Ontario, however, where government estimates for the number of licensed and unlicensed HCC spaces are available, revealed that more parents report that their children are in licensed HCC than is possible. The lack of accurate parental reporting calls into question a key assumption of current regulatory systems, which is that parents are informed consumers of ECEC services. Given that many parents misreport the type of HCC their children use, and the equity concerns raised by the overall utilization patterns we found, we argue that governments need to take a more active role in oversight and support of HCC.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.450
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
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.021
GPT teacher head0.318
Teacher spread0.296 · 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.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations23
Published2019
Admission routes2
Has abstractyes

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