HOW DOES LEARNING ANTI-RACIST EDUCATION THROUGH CRITICAL PROFESSIONAL DEVELOPMENT AND ARTS-BASED INQUIRY CONTRIBUTE TO TEACHERS’ UNDERSTANDINGS OF THEIR CLASSROOM PRACTICE?
Bibliographic record
Abstract
Racism is a significant problem within education. The education system has established institutional hierarchies and perpetuated different forms of racism that restrict the learning experiences of diverse students across Canada. One of the ways that teachers address and disrupt racism is through engaging in critical anti-racist professional development. This study gave teachers the chance to engage in critical anti-racist professional development where they learned about race, racism, and anti-racist education while exploring how their positionalities affect classroom practices. This study used a qualitative arts-based methodology called a/r/tography to create a space where teachers could have deep exploration and understanding of themselves and others by re-imagining lived experiences in and through time through the use of visual art. Combining a/r/tography and anti-racist education together it gave teachers the opportunity to develop awareness of their positionalities, become more critically conscious of their own racialized lenses, and understand how these lenses impact their classroom practices. Through critical artistic reflection on their practice in conjunction with learning anti-racist education strategies, teachers learned to reflect on their practices so as to become more aware of how to disrupt racism and white supremacy within their classrooms.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".