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Record W2030826968 · doi:10.1108/20426781211207674

Understanding adolescent sport participation through online social media

2012· article· en· W2030826968 on OpenAlexaff
Norm O’Reilly, Ida E. Berger, Tony Hernández, Milena M. Parent, Benoît Séguin

Bibliographic record

VenueSport Business and Management An International Journal · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsToronto Metropolitan UniversityUniversity of Ottawa
Fundersnot available
KeywordsNetnographySocial mediaPublic relationsOriginalityFocus groupContent analysisSociologyContext (archaeology)Value (mathematics)Political scienceQualitative researchSocial scienceComputer science

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to focus on the potential role and use of online social media to influence sport participation in youth aged 12 to 17 years by responding to two specific research questions: what is the nature of the online “marketplace” among youth?; and what is the nature of adolescent sport behavior as revealed through activities on online social media? Design/methodology/approach The paper outlines and then implements the research methodology of netnography to achieve its purpose. Netnography involves a researcher joining an online forum, e‐tribe or other open‐source social media to observe and record the discussions for analysis. Findings The overarching finding is that online discourse related to sport participation among youth is very limited. When discussion does take place, five themes emerge: benefits, advice‐seeking, finding common interests, learning new sports, and challenges. Research limitations/implications This research provides impetus for future work in the content area and in the use of the netnography method. It is limited by the lack of online content on the topic area by the target group. Practical implications The paper's results provide important understanding, direction and guidance to sport administrators working for government, sport organizations and organizations who market their products and services to youth through sport. Originality/value This paper is original in two respects: the use of netnography as the research method in this context, and the focus on social media and sport participation in youth.

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 score0.515
Threshold uncertainty score0.525

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 studies0.0010.000
Scholarly communication0.0000.001
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 head0.174
GPT teacher head0.373
Teacher spread0.199 · 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

Citations31
Published2012
Admission routes1
Has abstractyes

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