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From Copyright Cartels to Commons and Care: A Public Infrastructure Model for Canadian Music Communities

2022· article· en· W4290086391 on OpenAlexafffundvenueabout
Brianne Selman, Brian Fauteux, Andrew deWaard

Bibliographic record

VenuePartnership The Canadian Journal of Library and Information Practice and Research · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicMusic History and Culture
Canadian institutionsUniversity of AlbertaUniversity of Winnipeg
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCommonsConsolidation (business)Variety (cybernetics)Corporate governanceGovernment (linguistics)Public domainPublic relationsLivelihoodMusic industryPublic policyPoliticsBusinessPolitical sciencePublic administrationSociologyEconomicsMusic educationEconomic growthFinanceGeography

Abstract

fetched live from OpenAlex

Using research on the political economy of the music industries, interviews with independent musicians about their lived experiences, and the authors' experience participating in government copyright consultations in Canada, this article discusses how the market power of major music companies, and their capture of the policy-making process through lobbying, has made copyright reform an extremely limited avenue for remedying the variety of hardships facing musicians in the streaming media era. Against the continued consolidation and concentration of power within the music industries, we explore a case study of Edmonton Public Library’s Capital City Records as an alternative model that may inspire further initiatives that advocate for artists and users. We conclude by discussing a commons-based, public infrastructure and governance model that could serve as a tool to circumvent uneven power dynamics in the music industries, facilitate stronger music communities, and provide sustainable livelihoods for working musicians in Canada.

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 categoriesScience and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.657
Threshold uncertainty score1.000

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 studies0.0060.000
Scholarly communication0.0010.007
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.116
GPT teacher head0.290
Teacher spread0.174 · 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.

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

Citations4
Published2022
Admission routes4
Has abstractyes

Explore more

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