A Voice in the Wilderness: A Singer's Guide to the Implications of Performance Context in R. Murray Schafer's Wolf Music
Bibliographic record
Abstract
This dissertation investigates some of the challenges and opportunities inherent in the performance of Canadian composer R. Murray Schafer’s environmental compositions for female solo voice. Of particular relevance to this project are the vocal selections from Wolf Music (1996), a collection of pieces from Schafer’s Patria: The Epilogue; And Wolf Shall Inherit the Moon (1983- ). This dissertation aims to facilitate and improve performances of these pieces by providing insight and practical advice to their potential singers. Chapter One looks at how Schafer’s artistic goals, his beliefs, and his professional preoccupations all intersect in his environmental music, and in Wolf Music in particular. The second chapter compares Schafer’s vocal music intended for indoor and outdoor performance to reveal context-related changes in both his compositional style and in the aesthetic values of the music. Chapter Three describes the Wolf Music vocal selections in their original And Wolf Shall Inherit the Moon context, revealing the distinctive ethos that this context facilitates, and the remarkable experiences it has made possible for performers. The fourth chapter provides practical solutions for managing some of the potential challenges of performing these pieces. The implications of the previous chapters are thrown into clear focus in Chapter Five, through my performance journals from preparing Ariadne’s Aria in indoor and outdoor contexts in 2016. Throughout the paper I argue that with these compositions, Schafer is attempting to create something more than a beautiful soundscape; rather he seeks to engage the performer in a process of interactive creation with the natural environment, an interaction intended to be transformative.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".