Understanding adolescent sport participation through online social media
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".