The Routledge Companion to Interdisciplinary Studies in Singing, Volume I: Development
Bibliographic record
Abstract
<p><em>The Routledge Companion to Interdisciplinary Studies in Singing, Volume I: Development</em> introduces the many voices necessary to better understand the act of singing—a complex human behaviour that emerges without deliberate training. Presenting research from the social sciences and humanities alongside that of the natural sciences and medicine alike, this companion explores the relationship between hearing sensitivity and vocal production, in turn identifying how singing is integrated with sensory and cognitive systems while investigating the ways we test and measure singing ability and development. Contributors consider the development of singing within the context of the entire lifespan, focusing on its cognitive, social, and emotional significance in four parts:<br> </p> <ul> <li>Musical, historical and scientific foundations</li> <li>Perception and production</li> <li>Multimodality</li> <li>Assessment</li> </ul> <p>In 2009, the Social Sciences and Humanities Research Council of Canada funded a seven-year major collaborative research initiative known as Advancing Interdisciplinary Research in Singing (AIRS). Together, global researchers from a broad range of disciplines addressed three challenging questions: How does singing develop in every human being? How should singing be taught and used to teach? How does singing impact wellbeing? Across three volumes, <em>The Routledge Companion to Interdisciplinary Studies in Singing</em> consolidates the findings of each of these three questions, defining the current state of theory and research in the field. <em>Volume I: Development</em> tackles the first of these three questions, tracking development from infancy through childhood to adult years.</p>
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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 | 0.009 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.013 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.019 | 0.022 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.272 | 0.141 |
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".