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Record W2621554613 · doi:10.18357/jcs.v37i1.15186

British Columbia's Full Day Kindergarten Program Guide: A Critical Race Analysis

2012· article· en· W2621554613 on OpenAlexaffvenueabout
Jennifer Carla Moule

Bibliographic record

VenueJournal of Childhood Studies · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEthnic groupRace (biology)Early childhoodGender studiesSociologyPsychologyGeographyDevelopmental psychologyPedagogyAnthropology

Abstract

fetched live from OpenAlex

In September 2011, all elementary schools in British Columbia, Canada began providing a full school day of kindergarten to its children. Prior to this, the majority of children experi-enced kindergarten in a half day for-mat, while the province provided a full day to children from “certain pop-ulations” of ethnicity, language, and ability. Supporters of the change pro-fess that a full day of kindergarten can be beneficial for all children, which will subsequently benefit the province in general. This paper aims to gain insight into how the contemporary discourses in early childhood educa-tion in relation to full day kinder-garten influence the racialized experi-ences of British Columbia's young children of colour. A textual analysis was conducted on the Full Day Kindergarten Program Guide (2010) from the lens of Critical Race Theory. The guide's ambiguous use of the con-cept of “culture” is highlighted.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.296

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.007
Science and technology studies0.0120.004
Scholarly communication0.0040.001
Open science0.0010.002
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 head0.026
GPT teacher head0.375
Teacher spread0.349 · 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 designQualitative
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

Citations1
Published2012
Admission routes3
Has abstractyes

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