Drawing on Diversity in the Arts Education Classroom: Educating Our New Teachers.
Bibliographic record
Abstract
Abstract In this article, the authors discuss their attempts to make antiracist multiculturalism a reality in their students’ future classrooms. They note that the literature is replete with examples of what not to do in trivializing curriculum, and they attempt here to take theory into praxis/practice by exposing and describing their strategies for engaging their students in antiracist multicultural understandings and activities. Multiple diverse narratives in the classrooms of Canadian schools provide countless opportunities for arts educators to bring community into the classroom. However, research continues to show that new teachers still come predominantly from the dominant culture and will likely continue dominant traditions unless interventions occur that cause them to reflect on what and how they teach (Beynon, Veblen, & Bradford, in review). Realities of schooling and the crosscurrents of race, gender, and class compel educators to rethink culturally responsive curriculum. In this paper we describe the strategies that each of us used with prospective arts teachers in our university classrooms to educate them about issues of diversity and the necessity for inclusion. The purpose of this paper, then, is to explore means of bringing pedagogical
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.032 | 0.016 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.008 |
| 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".