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Record W2321280756 · doi:10.1386/ijcm.6.2.141_1

From dabbler to serious amateur musician and beyond: Clarifying a crucial step

2013· article· en· W2321280756 on OpenAlexaff
Robert A. Stebbins

Bibliographic record

VenueInternational Journal of Community Music · 2013
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAmateurCasualPerspective (graphical)Set (abstract data type)Field (mathematics)PsychologySociologyWork (physics)AestheticsEpistemologyVisual artsPolitical scienceArtLawEngineeringComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

Abstract Dabbling is casual leisure, a hedonic activity common in music as well as in a number of other free-time interests. In the past, dabbling has been inadequately conceptualized, one result being some misunderstanding about its nature and its contribution to leisure and even to professional work. By way of clarification we look first at the contemporary explanation of dabbling as set out according to the serious leisure perspective, a main analytic framework in the field of leisure studies. This explanation proceeds from two related articles written by Gates and Jorgensen. Next, dabbling as a leisure activity in music is considered in detail, particularly as it relates to children. This includes an examination of its nature and its role in initiating a leisure/work career in music, as experienced in the passage from dabbler to neophyte amateur and on possibly to professional.

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)0.010
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0110.037
Scholarly communication0.0090.015
Open science0.0020.007
Research integrity0.0080.016
Insufficient payload (model declined to judge)0.0040.001

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 head0.038
GPT teacher head0.326
Teacher spread0.288 · 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

Citations13
Published2013
Admission routes1
Has abstractyes

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