Black Mothers Enacting Refusal in Early Childhood Education and Care
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.039 | 0.031 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".