Antiracist pedagogy in post-secondary early childhood education programs: a critical inquiry into eight graduates' perspectives
Bibliographic record
Abstract
This critical qualitative research combines the narrative design of semi-structured interviews and artifacts collection to investigate what eight early childhood educators (ECEs) learned about antiracist pedagogy in their preservice ECE programs in Ontario. Researchers indicate that ECEs do not receive adequate education on antiracist pedagogy and may feel hesitant to address topics of race, racism and power in their practices. Guided by the theoretical framework of critical race theory (CRT) and antiracist education, the paper discusses the limitations of developmentalism and outlines the significance of preservice training on antiracist education. The findings reveal that all eight ECE participants did not learn anything explicit about antiracist pedagogy in their preservice ECE programs; rather, developmentalism dominated their postsecondary educational experiences. The study recommends that our postsecondary ECE programs need to move beyond a narrow vision of inclusion and developmentalism to envision childhood through diverse perspectives. Preservice postsecondary education must infuse antiracist pedagogy in its educational policies, hiring practices, and curriculum framework to counteract racism and advance social justice.
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 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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.016 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| 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".