A tale of two scenes: civic capital and retaining musical talent in Toronto and Halifax
Bibliographic record
Abstract
Although Toronto has been the centre of the Canadian music industry for many decades, recent interviews reveal that industrial restructuring may be affecting the choices that musicians make about where to live and work. In an era of contemporary independent music production, some smaller city‐regions, such as Halifax, Nova Scotia, are becoming more attractive to musicians. This article explores the ways in which musicians consider the economic and social dynamics of city‐regions in making their location choices. Musicians recognize Toronto's advantages in size and economic opportunity, yet those in the music scene described it as an intensely competitive and difficult work environment. By contrast, respondents in Halifax talked about a supportive and collaborative community that welcomed newcomers, encouraged performance, and facilitated creativity. In the contemporary context, where independent musicians are adopting new strategies to pursue their vocation, communities high in civic capital may gain an advantage in attracting and retaining talent .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".