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Record W2979663674 · doi:10.3138/cpp.2017-048

Introduction of Formal Child Care Services in Inuit Communities and Labour Force Outcomes

2019· article· en· W2979663674 on OpenAlexaffvenueabout
Donna Feir, Jasmin Thomas

Bibliographic record

VenueCanadian Public Policy · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsGovernment of Canada
Fundersnot available
KeywordsGraduation (instrument)Child careCensusDemographic economicsGeographyDemographyMedicineNursingEconomicsPopulationSociology

Abstract

fetched live from OpenAlex

We study the impacts of the introduction of formal child care services in 28 Inuit communities in Canada’s North. We use geographical variation in the timing of the introduction of child care services in the late 1990s and early 2000s to estimate the impact of increased access to child care. We combine the 1996, 2001, and 2006 long-form census files with data on the opening dates of child care centres in each of the 28 communities over time. We find little evidence of impacts on female labour force participation rates. Point estimates for other outcomes, including high school graduation rates and male participation in child care, are also mostly small and statistically insignificant. In many cases, subgroups in Quebec present the exception, potentially reflecting an interaction with the low-fee child care policy that was rolled out in Quebec over approximately the same time period. We do not find evidence that formal child care availability decreases the ability of children to speak Inuktitut. We suggest plausible explanations for these findings and avenues for future research.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.008
GPT teacher head0.254
Teacher spread0.246 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations5
Published2019
Admission routes3
Has abstractyes

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