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Record W4403089190 · doi:10.1080/00222216.2024.2402313

Ascending past constraints through immersion into the social worlds of climbing gyms

2024· article· en· W4403089190 on OpenAlexaffabout
Cory Kulczycki, Richard J. Buning

Bibliographic record

VenueJournal of Leisure Research · 2024
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsClimbingImmersion (mathematics)Social worldsPsychologySociologyComputer scienceHistoryMathematicsSocial scienceArchaeology

Abstract

fetched live from OpenAlex

Gyms are community hubs centered around physical activity and in recent years indoor climbing gyms have witnessed unprecedented growth globally as the sport has moved from extreme to the mainstream. As the sport continues to grow, the industry seeks to understand how to both keep new climbers and progress climbers already invested in the sport. The study sought to understand how individuals encountered and negotiated constraints as they became progressively immersed into the social worlds of the activity. Interviews were conducted with indoor rock climbers in Australia and Canada ranging in experience and social world progression (N = 27). The findings revealed the nature of constraints and the ways in which individuals negotiated them evolved as they become more immersed into the social community of climbing. Practically, this research provides the climbing industry with insight on how to assist in creating and maintaining climbing communities to foster continued participation.

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)0.004
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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0100.015
Scholarly communication0.0080.005
Open science0.0010.009
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.126
GPT teacher head0.489
Teacher spread0.362 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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