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Record W3004544486 · doi:10.1123/cssep.2019-0031

Extreme Sport, Identity, and Well-Being: A Case Study and Narrative Approach to Elite Skyrunning

2020· article· en· W3004544486 on OpenAlexaff
Kerry R. McGannon, Lara Pomerleau-Fontaine, Jenny McMahon

Bibliographic record

VenueCase Studies in Sport and Exercise Psychology · 2020
Typearticle
Languageen
FieldPsychology
TopicAdventure Sports and Sensation Seeking
Canadian institutionsLaurentian University
Fundersnot available
KeywordsNarrativeIdentity (music)PsychologyThematic analysisEliteNarrative inquiryContext (archaeology)AthletesSocial psychologySport psychologyNarrative identityConstruct (python library)Gender studiesSociologyAestheticsQualitative researchHistoryLiteraturePolitical scienceSocial scienceArtMedicine

Abstract

fetched live from OpenAlex

Although extreme-sport athletes’ experiences have been explored in sport psychology, more research is needed to understand the nuanced identity meanings for these athletes in the context of health and well-being. A case-study approach grounded in narrative inquiry was used to explore identity meanings of 1 elite extreme-sport athlete (i.e., skyrunner Kilian Jornet) in relation to well-being. Data gleaned from 4 documentary films and 10 autobiographical book chapters describing the Summits of My Life project were subjected to a thematic narrative analysis. Two intersecting narratives—discovery and relational—threaded the summits project and were used by Jornet to construct an “ecocentric” identity intertwined with nature in fluid ways, depending on 3 relationships related to well-being: the death of climbing partner Stéphane Brosse, team members’ shared values, and her relationship with partner Emelie Forsberg. An expansion of identity, health, and well-being research on extreme-sport athletes beyond simplistic portrayals of them as pathological risk takers and/or motivated by personality traits was gained from these findings.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.316
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.091
GPT teacher head0.390
Teacher spread0.299 · 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 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
Published2020
Admission routes1
Has abstractyes

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