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Record W3002420216 · doi:10.1177/2167479519899142

Five Rings, Five Screens? A Global Examination of Social TV Influence on Social Presence and Social Identification During the 2018 Winter Olympic Games

2020· article· en· W3002420216 on OpenAlexaboutno aff
Natalie Brown‐Devlin, Michael B. Devlin, Andrew C. Billings, Kenon A. Brown

Bibliographic record

VenueCommunication & Sport · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicMedia Influence and Health
Canadian institutionsnot available
FundersInternational Olympic Committee
KeywordsIdentification (biology)Social identity theoryChinaSocial capitalAdvertisingFandomSociologyPolitical scienceMedia studiesSocial groupBusinessSocial science

Abstract

fetched live from OpenAlex

This study uses social presence theory and social identity theory as theoretical frameworks to examine global social TV usage during a mega-sporting event. A total of 2,296 people from six different nations (Canada, China, Germany, Japan, Sweden, and the United States) were surveyed about their social TV usage, degree of social presence, and team identification in the week following the 2018 Winter Olympics in PyeongChang, South Korea. Primary findings illustrate that increased social TV use predicts increased measures of social presence, social capital, and perceived sociability, which then influences one’s identification with their national team and Winter Olympics fandom. Additional insights are found regarding global social TV engagement and ancillary device usage habits.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.050
GPT teacher head0.296
Teacher spread0.246 · 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 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

Citations17
Published2020
Admission routes1
Has abstractyes

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