Educating from Difference: Perspectives on Black Cultural Art Educators’ Experiences with Culturally Responsive Teaching
Bibliographic record
Abstract
Since the 2009 Ontario Ministry’s Equity and Inclusive Education strategy called for the implementation of culturally responsive pedagogy across the board, the voice of the Black creative and content in arts curriculum remains invisible. This primary research centers on the perspectives of African, Caribbean, and Black artists called on for the first time to discuss the successes and challenges of teaching culturally responsive arts in diverse Ontario classrooms. This qualitative study uses critical race theory to examine their experiences of working in Greater Toronto schools and surrounding areas in Ontario, Canada. Using cultural arts from across the African diaspora as a tool, their artistic work is situated within culturally relevant pedagogy, which is an alternative approach to centering on identity, cultural frames of reference, and critical student learning. Yet, as these Black Canadian artists garner successful impacts from culturally responsive teaching in classroom space, they identify simultaneous challenges of institutional unpreparedness, anti-Black racism, cultural appropriation, and legitimizing their cultural artistry to school administration. Semi-structured interviews include rich narratives from artists specializing in contemporary and traditional expressions of orality, visual arts, dance, and drumming/percussion from the African diaspora. While navigating instances of unbelonging, recommendations are proposed to improve the understanding of the artists’ role and improve Canadian educational institutions’ relationship with Black creatives in inclusive education.
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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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.082 | 0.045 |
| Scholarly communication | 0.020 | 0.007 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".