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Record W6932185033 · doi:10.5683/sp3/lqidlu

Survey on Early Learning and Child Care Arrangements, 2019 [Canada]

2019· dataset· en· W6932185033 on OpenAlexaffabout

Bibliographic record

VenueBorealis · 2019
Typedataset
Languageen
FieldMathematics
TopicStatistical Methods and Applications
Canadian institutionsStatistics Canada
Fundersnot available
KeywordsChild carePandemicFace (sociological concept)Data collectionCover (algebra)Child development

Abstract

fetched live from OpenAlex

Statistics Canada is gathering information from families who use child care as well as those who do not. The survey, which addresses child care in Canada for children younger than 6 years old, asks about the different types of early learning and child care arrangements that families use, difficulties some families may face when looking for care, as well as reasons for not using child care. The survey will also cover the COVID-19 pandemic and its impact on child care.

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)0.002
metaresearch head score (Gemma)0.014
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: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.038
Threshold uncertainty score0.130

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.012
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0030.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0180.013

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 head0.057
GPT teacher head0.362
Teacher spread0.305 · 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
GenreDataset

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

Citations0
Published2019
Admission routes2
Has abstractyes

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