Control, Constraint, Convergence: Examining Our Roles as Generalist Teacher Music Educators
Bibliographic record
Abstract
This research explores the effects of institutional constraints on instructional practices in a preservice generalist teacher music education program in Ontario, Canada.Using Institutional Ethnography and document analysis of active texts, we, an adjunct and tenured professor, use our own experiences to elucidate the multiple points of control and constraint in which teacher education instructors operate.We examine the ways in which "official" documents, such as course outlines, activate institutional expectations and relations of power, and promote standardization (convergence).We explore factors that influence our curricular choices, pedagogical strategies, and occasional acts of resistance; and how these impact differently tenured and adjunct faculty.The paper includes an introduction to the Action Research project that sparked this inquiry, in which we are investigating generalist teacher confidence and engagement with teaching music in the elementary classroom.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.052 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".