The Effect of the Canada-U.S. Border on the Vancouver, BC, and Seattle, WA, Music Network
Bibliographic record
Abstract
This Border Policy Brief examines the degree of connectivity between the two music scenes of Seattle, WA and Vancouver, BC, which are less than 150 miles apart and share similar socio-demographic, economic, and regional characteristics. This Brief focuses on the effect of the Canada – U.S. border on the degree of connectivity between those music scenes and points out some opportunities and challenges faced by independent music artists in the early stages of their career, including Indigenous artists. The research presented here includes highlights from a broader project (see sidebar). The vitality of the music industry in the two city-regions of Seattle, WA and Vancouver, BC is the product of the cross-fertilization and growth that result from the attraction, retention, and infusion of new talent, ideas, styles, information, and investments. This music ‘ecosystem’ relies on a social and physical infrastructure, with venues of different sizes, genres, and importance, in a transnational setting that includes shared ownership of venues. The region’s audiences, patrons of the arts, institutional support, and a local and diverse talent pool contribute to thriving music scenes in both cities. Cross-border collaboration in music production, distribution, and events is also an important element to establishing touring networks, maximizing investment, and developing information networks that collaborate with other industries, such as film, television and the region’s booming tech sector.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".