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Record W4281723500 · doi:10.29333/ajqr/12130

Exploring Structure and Culture in the Lived Acculturation Experiences of Newcomer Varsity Athletes in Manitoba

2022· article· en· W4281723500 on OpenAlexaffabout
Craig Brown, Leisha Strachan

Bibliographic record

VenueAmerican Journal of Qualitative Research · 2022
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsAcculturationAthletesContext (archaeology)Thematic analysisMental healthImmigrationPsychologyPsychological resilienceSociologyGerontologySocial psychologyEthnic groupQualitative researchAnthropologyMedicinePolitical scienceGeographyPhysical therapyPsychiatry

Abstract

fetched live from OpenAlex

With the growth of immigration in the Canadian context, newcomer athlete acculturation has grown in importance, with implications for everyone involved in the host culture sporting context. The purpose of this project was to increase our collective understanding of newcomer athlete acculturation in Canada by exploring the transition and settlement experiences of seven newcomer varsity athletes in Manitoba. The guiding question for this study was: What are the acculturation experiences of newcomer varsity athletes in Manitoba? An interpretive thematic analysis of the data resulted in themes highlighting particular social elements of Manitoban culture (e.g., tight-knit pre-existing social groups), mental health and resilience, and interactions with host culture systems as key elements in how the newcomer varsity athletes experienced acculturation. Such articulations support perspectives calling for further examination of the roles of structure, mental health and general wellness, and the influence of host and home context culture in understanding newcomer athlete acculturation.

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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score0.194

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0190.009
Scholarly communication0.0040.001
Open science0.0020.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.550
GPT teacher head0.596
Teacher spread0.047 · 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

Citations3
Published2022
Admission routes2
Has abstractyes

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