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Record W2344881099 · doi:10.1123/tsp.2015-0132

Female Varsity Athletes’ Perceptions of How They Became Optimistic in Sport

2016· article· en· W2344881099 on OpenAlexaff
Hayley L. deBeaudrap, John G.H. Dunn, Nicholas L. Holt

Bibliographic record

VenueThe Sport Psychologist · 2016
Typearticle
Languageen
FieldPsychology
TopicOptimism, Hope, and Well-being
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsOptimismPsychologyAthletesPerceptionSocial psychologyNarrativeDevelopmental psychologyPhysical therapy

Abstract

fetched live from OpenAlex

The purpose of this study was to explore female varsity athletes’ perceptions of how they developed high levels of dispositional optimism in sport. Eighty-three female varsity athletes completed a domain-specific version of the Life Orientation Test (LOT: Scheier & Carver, 1985). Nine participants (M age = 19.33 years, SD = 1.5) who had high dispositional optimism in sport then completed individual semistructured interviews. Interpretive Phenomenological Analysis methodology was used. Results showed that during childhood, participants perceived that their parents were supportive, provided feedback, and allowed them to have choice over the sports they played. During adolescence, coaches began to play an important role and participants were also able to learn about being optimistic through the positive and negative experiences they encountered. During early adulthood, participants developed personal narratives about the ways in which they approached sport with optimism.

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)0.001
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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
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)0.0020.000

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 head0.025
GPT teacher head0.309
Teacher spread0.285 · 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

Citations6
Published2016
Admission routes1
Has abstractyes

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