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Record W4416199664 · doi:10.37291/2717638x.202563645

Creating innovative Canadian approaches to anti-racism in early childhood: Transformative practices for addressing and disrupting racism

2025· article· en· W4416199664 on OpenAlexaffabout
Niveditha Menon, Lyle Hamm, Manju Varma

Bibliographic record

VenueJournal of Childhood Education & Society · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsRacismTransformative learningMainstreamIndigenousMulticulturalismCritical race theoryIdeologyEquity (law)IntersectionalityCritical theory

Abstract

fetched live from OpenAlex

Canada projects itself as a land of opportunity, embracing inclusivity and multiculturalism in both education and broader societal discourse. However, this ideological framework obscures the realities of race and institutional racism, failing to address persistent inequities experienced by Indigenous, Black, and other racialized communities, including immigrants and refugees. In early childhood education, dominant discourses of diversity and color blindness reinforce the notion that young children are racially innocent and unaffected by race. Yet, reconceptualist early childhood scholars demonstrate that by ages three to four, children develop racial awareness, identification, and attitudes, positioning themselves within racial hierarchies. This recognition underscores the enduring and systemic nature of racism embedded in both formal and informal educational settings. This paper explores innovative anti-racist educational approaches that extend beyond individual and interpersonal racism to challenge structural and systemic inequities within educational institutions. Centering marginalized critical theories, including Indigenous epistemologies, Critical Race Theory, and critical pedagogy, frameworks often overlooked in mainstream early childhood education, we highlight transformative pedagogies and practices that disrupt racism and advance equity in diverse educational contexts.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.504
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.000
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 head0.051
GPT teacher head0.350
Teacher spread0.299 · 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 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

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

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