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Record W4381432090 · doi:10.1080/1612197x.2023.2219460

Migration and meaning: an exploration of elite refugee athletes’ transitions into the Canadian sports system

2023· article· en· W4381432090 on OpenAlexaffabout
Cole E. Giffin, Robert J. Schinke, Michel Larivière, Diana Coholic, Yufeng Li

Bibliographic record

VenueInternational Journal of Sport and Exercise Psychology · 2023
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsLaurentian University
Fundersnot available
KeywordsPsychologyThematic analysisMeaning (existential)EliteRefugeeSocial psychologyAthletesReflexivitySport psychologyQualitative researchSociologyPsychotherapistPolitics

Abstract

fetched live from OpenAlex

The purpose of this research was to explore 14 elite refugee athletes’ experiences of transitioning to the Canadian sports system and to examine the social contexts that enabled and constrained meaning, a psychological mechanism that facilitates adaptive cultural transitions. Framed within critical realism, arts-based conversational interviews were undertaken with the elite refugee athletes. Through a reflexive thematic analysis and Viktor Frankl’s theory of meaning, four themes (feelings of hope and empowerment, environmental challenges and adaptations, despair, and social support) were created to trace the fluctuations of meaning throughout the refugee athletes’ transitions into their new sports systems. The results are presented through a single polyphonic vignette to highlight and contrast the how interacting contextual factors of time within a new sport system, support, and structure of the receiving sport system, enabled athletes to find meaning within their experiences. The manuscript provides an initial immersion into elite refugee athletes’ experiences which may be used by sports psychology practitioners (SPPs) to inform meaning-based interventions that encourages such athletes to connect with values present in their lives.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.383
Threshold uncertainty score0.698

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.034
GPT teacher head0.340
Teacher spread0.307 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations14
Published2023
Admission routes2
Has abstractyes

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