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Record W4401185143 · doi:10.1080/1612197x.2024.2364633

Cultural variation in self-assessment in athletes: towards the development of culturally-grounded approach in sports psychology

2024· article· en· W4401185143 on OpenAlexaff
Yuto Yasuda, Takahiko Masuda

Bibliographic record

VenueInternational Journal of Sport and Exercise Psychology · 2024
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAthletesPsychologySport psychologyPsychological interventionSocial psychologyApplied psychologyCultural diversitySelf-determination theoryVariation (astronomy)Developmental psychology

Abstract

fetched live from OpenAlex

Sports psychologists have assumed that, to achieve success in sports, all athletes must motivate themselves by viewing themselves positively (self-enhancement) and objectively analyzing their weaknesses to improve their performance (self-improvement). However, cross-cultural psychologists revealed that the type of motivation varies based on culture. North Americans tend to view themselves positively, whereas East Asians are likely to see themselves objectively. If these cultural variations are applicable to athletes, exploring how culture influences motivation in athletes will help create culturally fit interventions in sports. As such, the current studies examined cultural variations in motivation by selectively focusing on motivation in athletes participating in team and open-skills sports. Overall, the results revealed cultural variations in athletes’ motivation regarding their attention to positive and negative information about the self; as such, culturally fine-tuned theories for athletes are suggested.

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 score0.298
Threshold uncertainty score0.485

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.061
GPT teacher head0.402
Teacher spread0.341 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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