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Record W2036576169 · doi:10.1080/01490400.2013.761906

The Influence of Ethnicity and Self-Construal on Leisure Constraints

2013· article· en· W2036576169 on OpenAlexaffabout
Simon Hudson, Gordon J. Walker, Bonnie Simpson, Tom Hinch

Bibliographic record

VenueLeisure Sciences · 2013
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsEthnic groupPsychologySocial psychologySelf construalConstrual level theoryLeisure activitySociologySocial scienceAnthropology

Abstract

fetched live from OpenAlex

This study examines the effects of ethnicity, participation, and self-construal on constraints to the popular leisure activity of downhill skiing, an activity that is struggling to attract ethnic minority group members in North America. A new leisure constraints model guided our study, a framework that recognizes the importance of macro- (i.e., ethnicity) and micro-level (i.e., participation, self-construal) variables on the traditional concepts of intrapersonal, interpersonal, and structural constraints. After sampling both Chinese- and Anglo-Canadian skiers and nonskiers, results indicate that ethnicity does influence leisure constraints, both alone and in interaction with self-construal.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.021
GPT teacher head0.308
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 teacher head, not a consensus.

Study designObservational
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

Citations29
Published2013
Admission routes2
Has abstractyes

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