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Record W3200982355 · doi:10.1080/02614367.2021.1980087

From serious leisure to devotee work: an exploratory study of yoga

2021· article· en· W3200982355 on OpenAlexaff
Huimei Liu, Yan Huang, Mingjun Gao, Robert A. Stebbins

Bibliographic record

VenueLeisure Studies · 2021
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCasualWork (physics)Perspective (graphical)Leisure studiesSociologySociology of leisurePsychologySocial psychologySocial scienceTourismHistoryPolitical scienceEngineeringVisual arts

Abstract

fetched live from OpenAlex

This study aims to explore the serious leisure to devotee work (SL-DW) transformation trajectory in the case of yoga. An in-depth semistructured interview was conducted with 15 serious yoga practitioners to examine career change and the relationships between leisure and work. The findings of the research outline the passage from stepping into casual yoga leisure, moving from casual to serious yoga leisure, aspiring to succeed on the professional level, becoming an occupational devotee and finally a possible return to the serious-leisure state. A grounded theoretic model emerges explaining the blurred relationships between leisure and work, and the intertwined, dynamic and interactive nature serving as both the antecedent to and consequence of each other. This study theoretically contributes to deepening serious leisure career, the leisure-work relationships and the Serious Leisure Perspective. Meanwhile, it has practical implications for understanding and policy making for other emerging, leisure-oriented kinds of work in the new era.

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)0.003
metaresearch head score (Gemma)0.007
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 score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0070.006
Scholarly communication0.0040.003
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.072
GPT teacher head0.367
Teacher spread0.295 · 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

Citations17
Published2021
Admission routes1
Has abstractyes

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