Bibliographic record
Abstract
T here are a number of research publications that investigate various aspects of music education, ranging from philosophies of music education to analyses of the tiniest components of the discipline.While at a first glance this manuscript may seem to be just another "research in music education" publication, it is significantly different in a number of ways.First and foremost, it is the first and only collection of papers we are aware of that addresses the phenomenon of music education in Canada as inclusively as possible.Second, the authors represented in this publication are recognized as Canada's leading researchers in music education; they also represent music educators from the eastern-most point of Canada in Newfoundland to the western points of Vancouver Island, from the southern regions surrounding the Great Lakes and the United States border to the northern regions of Yukon and Nunavut.Finally, in each of the papers, the Canadian authors address one topic through a critical, but uniquely Canadian, lens.That is not to say that the content of these chapters is irrelevant to the rest of the world.What we mean is that Canadian music educators -professional and academic, musicians, and students -need not read this book and try to synthesize the content to our Canadian context, as is so often required with music education research.Canada is a vast country with a relatively sparse and clustered population.This book attempts to bring the huge and varied expanse of the current state of Canadian music education, as presented in the following integrated chapters, into somex i
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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 | 0.002 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.193 | 0.127 |
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".