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Record W3003203640 · doi:10.1123/iscj.2019-0026

“I Don’t Want to Give Them My Brain for the Day . . . and Then Take It Back”: An Examination of the Coach-Created Motivational Climate in Adult Adventure Sports

2020· article· en· W3003203640 on OpenAlexaff
Doug Cooper, Justine Allen

Bibliographic record

VenueInternational Sport Coaching Journal · 2020
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsDeer Lodge Centre
Fundersnot available
KeywordsAdventureCoachingPsychologyPerspective (graphical)AthletesApplied psychologySocial psychologyComputer sciencePsychotherapistMedicine

Abstract

fetched live from OpenAlex

In contrast to cross-sectional age trends of declining adult participation in sport, engagement in adventure sports is increasing among adults. The coach may have an important role to play in shaping the motivational climate to encourage and retain participants in adventure sport. The purpose of this study was to provide an in-depth examination of the coach-created motivational climate in noncompetition focused adult adventure sport by adopting a multiple methods approach. The study was grounded in a multidimensional theoretical perspective that combines achievement goal theory and self-determination theory. Questionnaires, interviews, and observations of coaching sessions were employed to assess coaches’ (N = 6), participants’ (N = 25), and observers’ perspectives on the empowering and disempowering nature and features of coaching sessions. Analysis of the data demonstrated consistent views that the coaches created a strongly empowering and only weakly disempowering climate. Insight was gained about why and how coaches created this climate, as well as the challenges they experienced in maintaining an empowering climate for adults in adventure sport contexts. The place of structure, control, relatedness support, and coaches’ philosophies are discussed.

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)0.002
metaresearch head score (Gemma)0.003
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.294
Teacher spread0.267 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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