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Record W2955385348

The Effect of the Canada-U.S. Border on the Vancouver, BC, and Seattle, WA, Music Network

2019· article· en· W2955385348 on OpenAlexaboutno aff
Nabil Kamel, Lindsey Nordby, Henry Haro, Claire Swearingen

Bibliographic record

VenueWestern CEDAR (Western Washington University) · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.833
Threshold uncertainty score0.819

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.208
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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