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Record W1559760526

Juxtaposing Aboriginal Hip Hop, Local Heavy Metal Scenes, and Questioning Public Recreation/Leisure Services

2011· article· en· W1559760526 on OpenAlexaffvenueabout
Karen M. Fox, Gabrielle Riches, Michael Dubnewick

Bibliographic record

VenueMUSICultures · 2011
Typearticle
Languageen
FieldArts and Humanities
TopicMusic History and Culture
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPopular musicScholarshipRecreationSociologyActive listeningAestheticsVisual artsMedia studiesArtPolitical scienceCommunicationLaw
DOInot available

Abstract

fetched live from OpenAlex

Listening to music and music-making are so ubiquitous that the spatio-temporal requirements for music-making is often overlooked. However, without the spatio-temporal contexts, especially leisure spaces and times, music cannot exist, because desire and imagination are aesthetically concentrated in leisure forms. Although differently positioned, both Canadian urban Aboriginal hip hop and local heavy metal cultures must struggle against numerous odds to simply make their music let alone find venues and audiences that can support musicians while resisting dominant societal forces to voice themes of diversity and social justice. If music-making is to be sustained and understood in its complexity, then the power of leisures (desires, pleasures, spatio-temporal contexts, and identities) needs to be included in studies of popular music, and popular music/music-making must become a greater focus of popular music and leisure scholarship and practice.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.663
Threshold uncertainty score0.678

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0160.020
Scholarly communication0.0090.002
Open science0.0010.007
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.000

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 head0.039
GPT teacher head0.229
Teacher spread0.190 · 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

Citations0
Published2011
Admission routes3
Has abstractyes

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