The role of pluralism in fostering an ethic of social justice: Policy recommendations for music therapy education and training
Bibliographic record
Abstract
Recent social justice-focused and anti-oppressive scholarship has called for broader and more intentional inclusion of critical analyses and constructivist epistemological frames to promote equity, diversity, inclusion, accessibility, and decolonisation in music therapy education and training. The recent expansion of social justice content in the revised code of ethics of the Canadian Association of Music Therapists (CAMT) is a step in the right direction. It requires certified music therapists to actively identify, understand, and eliminate implicit biases and discriminatory practices and to cultivate awareness of the harms that have been exacted by oppressive practices within and beyond the profession. We argue, here, that preparing music therapy students to meet professional standards of practice and adhere to the social justice-focused ethical principles articulated in the code of ethics requires Canadian music therapy education programs to intentionally integrate dissension as a key aspect of social justice work throughout their curriculum. In this critical contemplation, we posit that mobilising a commitment to social justice education must first and foremost be grounded in a pluralistic ethos, which values diverse ways of being, thinking, learning and knowing. We then explore the critical integration of lived knowledge, the notions of dignity safety and intellectual insecurity in educational spaces, and arts-based social pedagogies as potentially transformative practices in socially-just music therapy 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 | 0.095 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.031 | 0.110 |
| Scholarly communication | 0.043 | 0.048 |
| Open science | 0.007 | 0.036 |
| Research integrity | 0.049 | 0.038 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".