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Record W2293419748 · doi:10.5539/res.v8n2p1

Does Love Influence Athletic Performance? The Perspectives of Olympic Athletes

2016· article· en· W2293419748 on OpenAlexvenueno aff
Kelly Campbell, Cheyenne Hosseini, Kelly A. Myers, Nina Calub

Bibliographic record

VenueReview of European Studies · 2016
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsnot available
FundersNational Institute of General Medical Sciences
KeywordsAthletesPerspective (graphical)PremisePsychologyRomanceAssociation (psychology)Social psychologyEpistemologyPsychoanalysisPsychotherapistComputer scienceMedicine

Abstract

fetched live from OpenAlex

In this brief report, we provide an initial account of the association between love and athletic performance from the perspective of Olympic athletes. We posit that Romantic Passionate Love (RPL) and athletic performance may both involve the reward-motivation system of the brain. Based on this premise, we explored whether activation in one domain (love) might influence the other (sport). Our investigation was framed using Sternberg's triangular theory of love. Twenty Olympic athletes representing different sports were interviewed at the Games. Most athletes (n = 15) reported that their performance was better while in love; however, qualitative responses suggested that the benefits were correlated with rather than resulting from RPL. Although the athletes were provided with a definition of RPL and affirmed that their relationship met the criteria, interview responses reflected companionate rather than passionate love, suggesting that RPL may be differentially conceptualized across cultures. The study provides preliminary data that may be used to inform and refine future work on this topic.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.851
Threshold uncertainty score0.751

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.001

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 head0.059
GPT teacher head0.392
Teacher spread0.333 · 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

Citations13
Published2016
Admission routes1
Has abstractyes

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