Voice-in-the-World: An Exploration of Mid-Career Opera Singers’ Non-Musical Stressors and Coping Strategies
Bibliographic record
Abstract
Singers are constantly balancing how they, as subjects, interact with objects in the world to create a consistent product (sound). But singers are also used as objects in the creation of someone else’s work by composers, directors, and companies, etc. This results in a highly unstable and unpredictable career that can upset the inner balance between coping and resilience. Using Interpretative Phenomenological Analysis (IPA), this thesis explores the lived experience of seven elite-level Canadian opera singers through in-depth narratives to discover these performers’ non-musical stressors and coping strategies. The four emergent stress themes (finances, expendability, peripatetic lifestyle, and fatigue) were all accentuated by feeling a lack of control, or lack of agency, and instability. Often, the coping strategies were obfuscated by stress and were in fact harder for the singers to discuss or identify. The three main coping strategies outlined—routines and consistency, emotional self-management, and support network and communication—reflected how the singers tried to create stability and control to find resilience and longevity in the opera business. This thesis ends by bringing awareness to the idea of creator mind versus the singing body—a dichotomy which, I argue, leads to the subjugation of opera singers. This study is not meant to produce definitive conclusions or solutions; rather it is meant to prompt a conversation by focusing on what Heidegger (1927/2010) would call a caring “attunement” towards a deeper understanding of what it is to be an opera singer in Canada.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".