Bibliographic record
Abstract
The Council for Research in Music Education is pleased to announce the recipients of the Outstanding Dissertation Award for 2020–2021.Twenty-one dissertations were nominated before undergoing review by members of the Council for Research in Music Education and other scholars. Reviewers were asked to consider how each dissertation: Advanced overarching values of music education, broadly defined;Employed rigorous and/or innovative methodologies;Integrated research and theory grounded in a variety of intellectual traditions; andOffered insightful implications for improving practice or forwarding theoretical constructs.Kelly BylicaThe University of Western OntarioCritical Border Crossing: Exploring Positionalities Through Soundscape Composition and Critical ReflectionPatrick K. Schmidt, AdvisorLaurel ForshawUniversity of TorontoEngaging Indigenous Voices in the Academy: Indigenizing Music in Canadian UniversitiesLori-Anne Dolloff, AdvisorThank you to Council for Research in Music Education researchers who served on review panels and to Karen Blackall for her invaluable assistance with this project. Congratulations to these outstanding researchers, their advisors, and all those who supported this program of scholarly recognition.
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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 | 0.022 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.021 | 0.006 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.336 | 0.258 |
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".