Centering Black women’s voices to advance anti-racist pedagogy and pro-Black approaches in early childhood settings
Bibliographic record
Abstract
Despite calls for anti-racist and pro-Black pedagogy in early childhood settings within Canada, such approaches generally remain absent, and many past efforts have been erased or. Colorblind approaches continue to persist in early childhood care settings. This kind of systemic racism may be linked with the overrepresentation of Black (and Indigenous) children in the child welfare system. To date, few studies have investigated the possible mediating role of early childhood educators. Early childhood educators have the potential to serve as mandated reporters, and this article draws from Black feminist thought, Black critical theory (BlackCrit) and pro-Black pedagogies to explore how Black early childhood educators in Toronto, Canada can challenge anti-Black racism in childcare settings. Using these frameworks, the article aims to explore the resistance strategies of Black early childhood educators and specific ways they use their knowledge to resist anti-Black racism and enact pro-Black pedagogies. Nine semi-structured interviews yielded four key themes: (1) teachable moments to challenge anti-Black racism and advance anti-racism; (2) pro-Black approaches as liberatory pedagogical practice; (3) othermothering principles and community leadership to challenge oppression; and (4) resistance practices among Black early childhood educators. The findings revealed how Black early childhood educators affirm Black children's humanity and center Black ways of knowing while disrupting anti-Black racism toward themselves and the children and families they support.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.040 | 0.028 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".