(Re)-enacting the Ontario Music Curriculum: Breaking the Cycle of Injustice
Bibliographic record
Abstract
Within Ontario and under ideal circumstances, music education is a practice that is inherently community driven and culturally relevant, critically intertwined with identity, community building, and cultural sharing. Despite the pressing need for diversity, equity, and inclusivity within primary and secondary education, the Ontario Arts Curriculum (namely music) has not been revised within its recommended 10-year period. This has left Ontario arts educators adrift with little guidance regarding how to mandate equitably just teaching practices. The aim of this research, then, is to provide music educators with some of the tools required to interpret the Ontario Arts Curriculum so as to better facilitate a form of music education that celebrates cultural difference and social justice. This includes an analysis of current classroom practices surrounding music theory, history, and performance, to seek ways in which these subjects can be made more equitable, diverse, and inclusive for all using tools such as popular music and technology. The subject is approached through the work of Paulo Freire and encapsulates how those who are colonized and oppressed can take back their own voices within their educational learning by reimagining education as dialogical and problem- posing, encouraging students to engage with their personal views of the world and their own interests and histories. Thus, it is critical that the Ontario Arts Curriculum is examined through a Freirean perspective in an exploration of diverse, equitable, and inclusive classroom teachings to better music education practices for Ontario students of all backgrounds.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.030 | 0.030 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".