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Leisure Music Production

2017· book· en· W2588084237 on OpenAlexaff
Robert A. Stebbins

Bibliographic record

VenueOxford University Press eBooks · 2017
Typebook
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAmateurSpace (punctuation)Meaning (existential)JazzMusicalAestheticsSet (abstract data type)Core (optical fiber)Visual artsSociologyTourismAdvertisingPsychologyArtEngineeringHistoryComputer scienceBusiness

Abstract

fetched live from OpenAlex

Making music is largely a serious leisure activity, and as with all serious leisure it takes place in a set of physical spaces, each with its own special meaning for the participant. That meaning has roots in the core activities conducted there. Stebbins has identified and explored seven types of spaces for serious leisure, six of which pertain to making serious music. They are conquered space, showcase space, resource space, sales space, helping space, virtual space, and tourist space. Each is discussed in detail sufficient to show how it can help explain the lifestyle of the modern amateur and professional musician. The main types of today’s musician are included in this analysis: jazz, rock, folk, and classical. Furthermore, the analysis proceeds from the core activities comprising the musical life, as these unfold in the different places where that life occurs.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.092
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.073
GPT teacher head0.249
Teacher spread0.176 · 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
GenreOther

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

Citations7
Published2017
Admission routes1
Has abstractyes

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