Privileging Power: Early Childhood Educators, Teachers, and Racial Socialization in Full-Day Kindergarten
Bibliographic record
Abstract
This paper critically unpacks the racialized and gendered hierarchies between the co-teaching model of early childhood educators (ECEs) and Ontario certified teachers (OCTs) in full-day kindergarten (FDK), and how such positionalities speak to racial socialization in early learning spaces. While young children and early learning spaces are often portrayed as raceless, ahistorical, and apolitical, extant literature suggests that children as young as two years of age are aware of visible and cultural differences between themselves and other groups. The paper employs a reconceptualist framework by drawing on critical race theory to explorehow racialized power relations between ECEs and teachers inform hierarchies of dominance and impact processes of racial socialization in FDK learning spaces. While both professions are predominantly feminized, the overwhelming majority of teachers in Ontario are white and middle class, whereas ECEs in FDK programs are more likely to be racialized and marginalized due to low wages and diminished professional status as care workers rather than educators. Although there has been great emphasis on the importance of diversifying the teacher workforce, there is minimal study on the impact of the hierarchies and racialized power relations between ECEs and OCTs and their impact on racial socialization in FDK programs. This conceptual paper seeks to address this gap.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".