MétaCan
Menu
Back to cohort
Record W4293851115 · doi:10.32731/smq.313.0922.05

Patriot, Expert, or Complainer? Exploring How Athletes Express Themselves at Olympic Games’ Press Conferences

2022· article· en· W4293851115 on OpenAlexaff
Bo Li, Olan Scott, Stirling Sharpe, Sarah Stokowski, Qian Zhong

Bibliographic record

VenueSport Marketing Quarterly · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsBrock University
Fundersnot available
KeywordsMedalPresentation (obstetrics)BasketballAthletesPromotion (chess)AdvertisingPolitical scienceParadeLeagueMedia studiesPublic relationsSociologyPsychologyHistoryLawBusiness

Abstract

fetched live from OpenAlex

Media coverage of the Winter Olympic Games provides an invaluable opportunity for athletes to promote themselves to a global audience that otherwise would not be reached through regular calendar events. An important element of athlete promotion occurs during press conferences when athletes speak to global media after winning a medal. The purpose of this study was to investigate how Olympic medalists presented themselves in front of the media after achieving Olympic success. A thematic analysis was conducted using press conference transcripts from 307 Olympic medalists during the 2018 PyeongChang Winter Olympic Games. The results indicated that athletes were likely to use media opportunities to self-promote their achievements, share secrets and stories, exhibit gratefulness, protest, show patriotism, and provide expert opinion. Overall, six categories of self-presentation were identified and discussed. Practical and theoretical implications are offered throughout, including a contribution to self-presentation theory and suggestions for athlete media literacy training.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.828
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.081
GPT teacher head0.293
Teacher spread0.212 · 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.

Study designNot applicable
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

Citations5
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueSport Marketing QuarterlySame topicSport and Mega-Event ImpactsFrench-language works237,207