Politicizing Early Childhood Education and Care in Ontario: Race, Identity and Belonging
Bibliographic record
Abstract
The Early Childhood Education and Care (ECEC) landscape, much like the K-12 education system in Ontario, is largely encompassed by bias-free, neutral and colourblind narratives of identity and social location (Author 1, 2018). These discursive practices portray young children and early learning settings as raceless and equal spaces that engage children in interactions and discussions of race and identity are inappropriate. Education in Ontario and Canada as an entity is marked by myth of the Canadian nation-state (Thobani, 2007) through celebratory, themed, recognition-based initiatives that mark differences, while leaving the status quo of whiteness unchallenged and intact (DiAngelo, 2018). The objective of the paper is to challenge discursive norms that perpetuate the dominant norm that young children do not see or notice race and are insulated from processes of racial socialization, through a reconceptualist framework. The paper does this by centering the socialization of race and identity in Ontario, Canada’s most diverse province and one of the most ethno-racially diverse regions in the world. This paper not only disputes the common misconception that ECEC sites are neutral spaces, but also re-centers these spaces as political as well as potential sites of resistance.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.032 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".