Sounding Spaces: exploring interactions among space, place, music and identity in a Canadian community choir
Bibliographic record
Abstract
This dissertation, situated in St. John’s, Newfoundland and Labrador, is a musical geography and ethnography of Lady Cove Women’s choir that explores interactions among music, identity, and place. In this study, I consider how choral musicians use music to negotiate individual and collective identities as they interact with material and conceptual spaces of music-making. Participant observation and interviews were used together with conceptual mapping and participant-led tours to explore the kinds of spaces musicians inhabit in the city; their subjective senses of space and place and differences of spatial knowledge and affective responses to space and place. The study enabled participants to connect musical experiences to their material environments and associated identities, memories, emotions and relationships. I use Lefèbvre (1974) and Massey (1992, 2005) to contextualize participant experiences in terms of urban change and in relation to theoretical approaches that highlight the relational nature of space. Like Massey (1994), I frame space and place in terms of social relations to consider questions of accessibility and spatial organization, discussing how spaces are marked by uneven patterns of ideology, power and wealth. Lefèbvre’s theory of space as socially produced and contingent informs my discussion of evolving senses of place in response to urban change, shifting demographics, new economic realities and natural resources. The study revealed several key findings. First, it mapped how musicians move through urban space, revealing how routes are carved into the city over time and demonstrating genre-influenced patterns of visiting and re-visiting material music-making spaces like church buildings and arts centres. Second, analyzing the locations in participant tours illuminated what matters to participants and why, what made certain places distinctive or important for them and how these places changed over time. Finally, the diverse perspectives presented in participant narratives helped to highlight the multiple and relational nature of social space and offered opportunities to explore the ways in which locations of community music are animated by competing, sometimes conflicting discourses and spatial practices.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.020 | 0.012 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".