How Unpopular Musicians Live: Complex Leisure Careers in Toronto's Indie Scene
Bibliographic record
Abstract
The phenomenon of independence in popular music offers a glimpse into the economic dimensions of musical creation and livelihoods. Such considerations have been absent in scholarship concerning musical learning and music education. Building upon extant research on popular music, this research seeks to further the growing presence of (un)popular music education emphasizing the reality that most musicians who engage in popular music are relatively unpopular. This narrative ethnographic research features the experiences and stories of 24 Toronto-based indie musicians. Data was collected via 17 semi-structured interviews between May and December of 2020. 13 interviews feature individual musicians, and four interviews were conducted with bands (11 musicians). The interviews were structured around open-ended questions that invited participants their musical histories and perspectives. A significant finding was the highly complex relationship between earning a living and pursuing a career creating original music, which one participant describes as a catch-22 due to the rising cost of living in Toronto. Further, the opaque gatekeeping processes of the music industry provide a deep contrast from the music scene, suggesting functional differences between the two despite significant overlap in their physical and social spaces. The analytical and theoretical framework for this research is based on extensions to Stebbins’ serious leisure perspective. The first is the expansion of possible activities to include fulfillment activities, entrepreneurial activities, ancillary activities, and casual activities; the second is the concept of a complex leisure career, containing multiple activities, which serves to better represent the complexity of indie musicians’ careers in creating original music; the third is the introduction of semi-professional devotee as a role to most respectfully and accurately represent the magnitude of musical activities (and complex leisure careers) in the participants’ lives. I conclude by discussing the implications of participants’ experiences on the field of music education. In considering the purpose of public music education, I suggest that school-based music education could better support the individual and collective interests of its students through the implementation of learning models based on serious leisure education, prioritizing students’ independence (of).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".