Hoarse with no name: chronic voice problems, policy and music teacher marginalisation
Bibliographic record
Abstract
The voice is arguably one of the most important tools of the trade for music teachers. However, vocal health for music teachers is often relegated to the margins of policy discussion. This article investigates the social and political environs where vocal health resides, arguing that music teachers must be the first advocates for the enforcement of labour policies wherein the centrality of who they are, their real and metaphoric voices, is the subject of greater care. The purpose of this article is threefold: (1) to establish voice care as a ground-level policy issue that confronts music teachers daily; (2) to stimulate discussion of the impact of voice care as an agency-filled pathway in the professional lives of current and future teachers; and (3) to facilitate access to advocacy and policy ideas that will help educators and decision-makers to see vocal health as significant in the construction of professional autonomy.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".