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Record W2606646193 · doi:10.14507/epaa.25.2685

The changing landscape of school choice in Canada: From pluralism to parental preference?

2017· article· en· W2606646193 on OpenAlexaffabout
Lynn Bosetti, Deani Van Pelt, Derek J. Allison

Bibliographic record

VenueEducation Policy Analysis Archives · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Education Studies and Reforms
Canadian institutionsFraser Institute
Fundersnot available
KeywordsPluralism (philosophy)Public educationSchool choiceProtestantismPreferencePublic fundingDescriptive statisticsSociologyPolitical scienceEconomic growthPublic administrationEconomicsLaw

Abstract

fetched live from OpenAlex

This paper provides a descriptive account of the growing landscape of school choice in Canada through a comparative analysis of funding and student enrolment in the public, independent and home-based education sectors in each province. Given that the provinces have responsibility for K-12 education, the mixture of public, independent and home school education varies rather widely by province, as does the level of funding and regulation. Delivery and funding of public education in Canada has long prioritized limited linguistic and religious pluralism, providing various options for English or French, and Catholic or Protestant alternatives to qualified parents. More recently growing numbers of parents have been seeking more options for their children’s education. This has fueled slow but steady growth in independent schools and home schooling.

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)0.005
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 score0.107
Threshold uncertainty score0.779

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.007
Science and technology studies0.0100.008
Scholarly communication0.0060.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.037
GPT teacher head0.347
Teacher spread0.310 · 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

Citations22
Published2017
Admission routes2
Has abstractyes

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