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Record W2585750058 · doi:10.5430/ijba.v8n1p118

Becoming Sport Fans: Relative Deprivation and Social Identity

2016· article· en· W2585750058 on OpenAlexvenueno aff
Yong Chae Rhee, John B. Wong, Yukyoum Kim

Bibliographic record

VenueInternational Journal of Business Administration · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsnot available
Fundersnot available
KeywordsRelative deprivationRepresentativeness heuristicIdentification (biology)Social identity theoryIdentity (music)Professional sportIrrational numberPsychologySocial psychologyTeam sportAthletesSocial groupLeagueMedicine

Abstract

fetched live from OpenAlex

This study explores how people become sport fans by elucidating why people support teams even when they are unsuccessful. This study fills a gap in the literature on sport fan behavior by applying Relative Deprivation and Social Identification Theories to understand sport fans’ seemingly irrational behavior. We conducted a series of interviews with 17 sport fans with diverse backgrounds. Findings suggest that interaction among Community Identification, Relative Deprivation, Team Identification, Sport Involvement and Representativeness of a sport team helps explain why people support certain teams and become fans, regardless of team success. Findings suggest that team Representativeness in a specific community is one of the most important factors influencing people to become fans. We also found that sport involvement is very important, especially if relative deprivation can elicit team identification from people with little to no sport involvement. Further research may identify the exact relationship between sport involvement and relative deprivation.

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 score0.661
Threshold uncertainty score0.239

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 communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
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 head0.047
GPT teacher head0.351
Teacher spread0.304 · 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

Citations9
Published2016
Admission routes1
Has abstractyes

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