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Record W2891519038 · doi:10.1123/jsm.2017-0263

The Reverse Socialization of Sport Fans: How Children Impact Their Parents’ Sport Fandom

2018· article· en· W2891519038 on OpenAlexaff
Craig Hyatt, Shannon Kerwin, Larena Hoeber, Katherine Sveinson

Bibliographic record

VenueJournal of Sport Management · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsUniversity of ReginaBrock University
Fundersnot available
KeywordsFandomSocializationPsychologyAdvertisingSocial psychologySociologyMedia studiesBusiness

Abstract

fetched live from OpenAlex

While the sport fan literature suggests that it is common for parents to socialize their children to cheer for specific sports and teams, recent literature proposes that children can socialize their parents into changing the parents’ sport fandom in a process sociologists and consumer behavior researchers refer to as reverse socialization. To ascertain whether children can socialize and influence their parents’ sport fandom, 20 sport fan parents were interviewed. Evidence of reverse socialization was found in 15 of the participants, manifesting itself in ways that can be categorized as either developing new or additional fandom, or changing one’s behaviors or attitudes towards their existing fandom. However, further exploration of the data suggests that future research reexamine the term “reverse socialization,” as we do not see this as a directionality of influence, but as children as socializing agents.

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)0.005
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 score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.294
Teacher spread0.274 · 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

Citations47
Published2018
Admission routes1
Has abstractyes

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