Preparing Music Educators to Work with Students with Diverse Abilities: An Introduction to Music Therapy
Bibliographic record
Abstract
Music education programs are uniquely situated within Canadian universities as most disciplines do not offer honours education programs at the undergraduate level. Within faculties of music, honours music education students engage in both practical and philosophical preparation for their teaching careers prior to acceptance and enrolment at a Faculty of Education. These students often return to departments of music education to pursue graduate work after having taught music within public or private school systems.\nMusic teachers regularly teach children with special needs within self-contained as well as integrated or inclusive classrooms. Research indicates that music educators are enthusiastic about the prospect of teaching children with diverse needs but feel underprepared as to how to teach them effectively. Music therapists have specific training in using music with individuals who have diverse needs so as to help these specific individuals accomplish goals in both musical and non-musical domains. This introductory workshop, led by a music therapist, will develop graduate students’ understanding of music therapy and introduce them to techniques based on music therapy literature. Relevant also for undergraduate music education students, as well as for pre-service and practicing teachers, this workshop addresses ways to further cultivate practical skills that are useful for any music educator. Although the content is specific to working with children in self-contained special-needs classes, applications to inclusive classrooms will be acknowledged throughout this workshop as well.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".