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Record W2752044672 · doi:10.1386/mms.3.3.369_1

The entrepreneurial imperative: Recording artists in extreme metal music proto-markets

2017· article· en· W2752044672 on OpenAlexaff
Jason Netherton

Bibliographic record

VenueMetal Music Studies · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicMusic History and Culture
Canadian institutionsWestern University
Fundersnot available
KeywordsMusic industryRevenueSubjectivityPopular musicVisual artsSociologyArtPublic relationsAestheticsPolitical scienceBusinessMusic education

Abstract

fetched live from OpenAlex

Abstract Recent research on recording artist entrepreneurism suggests that ‘emerging music professionals need an entrepreneurial spirit’ and that ‘they need to think like an entrepreneur (even if some don’t like the term) to sustain a career in the diverse fields of the music industries’. If recording artists are now facing pressure to be both creative and entrepreneurial subjects, how does this duality then impact many of the traditional expectations and practices of recording artistry and the institutions that surround them? As a case intended to explore this question, this article will examine recording artist entrepreneurism through the economic and scenic practices occurring in extreme metal music proto-markets. Specifically, analysis will focus on the case of the Australian band Ne Obliviscaris, who were the first extreme metal act to successfully use the online patronage platform Patreon. Ne Obliviscaris’ turn to Patreon is representative of a broader transition towards recording artist entrepreneurism, where new funding and revenue options are impacting the traditional relations between the artist, the record label and the audience. Entrepreneurism is therefore interpreted as an emerging institutional norm of recording artistry, with implications for recording artists’ subjectivity, expectations and social positions.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.023
Scholarly communication0.0120.005
Open science0.0010.009
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.000

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 head0.198
GPT teacher head0.290
Teacher spread0.092 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations51
Published2017
Admission routes1
Has abstractyes

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