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Record W4408589756 · doi:10.32920/28624889.v1

Black Mothers Enacting Refusal in Early Childhood Education and Care

2025· preprint· en· W4408589756 on OpenAlexaboutno aff
Janelle Brady, Rachel Berman

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicYouth Substance Use and School Attendance
Canadian institutionsnot available
Fundersnot available
KeywordsEarly childhood educationEarly childhoodChild carePsychologyDevelopmental psychologyPolitical scienceMedicineNursing

Abstract

fetched live from OpenAlex

<p>Anti-Blackness enacted against children in early childhood education and care (ECEC) has been well documented in the context of the United States, particularly in recent years, where young Black children have been belittled by teachers, more harshly punished than their white peers, and expelled, leading to what’s been called the “preschool-to-prison pipeline.” Anti-Black racism and possibilities for Black futurity in the Canadian context of ECEC (pre-Kindergarten), however, remain largely unconsidered. Through the frameworks of Black feminist theory, critical race theory, and Tina Campt’s concept of Black refusal, the authors explore the counter-stories and lived experiences of five Black mothers with children attending childcare centres in the Greater Toronto Area. The contributions of the mothers are organized into five themes: Black mothers refusing tropes of neglectful and angry Black women; Black mothers refusing the pathologization of their children and offers for liberatory re-imaginaries; Black mothers refusing the erasure of Black cultures and living Black futures; Black mothers refusing the status quo in ECEC and offering alternatives; and Black mothers refusing anti-Black racism against children through nuanced dialogue and alternatives. The authors conclude by discussing the possibilities of refusing unjust systems and reimagining alternatives that centre Black affirmation and liberation.</p> <p> </p>

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.229
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.308
Teacher spread0.292 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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