Examining the Outcomes of Sport Specialization for Individual Athletes and the Industry
Bibliographic record
Abstract
Sport specialization for young athletes has become a prerequisite for sport achievement, but academics have yet to explore the effects that sport specialization has on athletes’ participation patterns. Thus, this project explores the following research question: what are the effects of sport specialization on the individual volleyball athlete in terms of: i) patterns of participation in sport; and ii) consumption patterns in the sport industry? The methodological approach was to interview current and retired volleyball players aged 18 to 30 in Calgary, Alberta. The findings indicate that specialization in volleyball directly impacts an athlete’s patterns of participation in the sport of volleyball and the sport industry broadly. Participants indicated that their specialization years led to a specialized “mindset” and specialized knowledge, a unique analytical experience that influences sport participation and few individuals outside of the specialized athletic community acquire. Many participants also articulated that specialized training led to an identity as a “volleyball player” which was associated with a reduced desire to participate in other sports recreationally. Many participants explained how specialization affected their socialization (both positively and negatively) and led them to foster connections in a virtual community. The findings are a call to action for the volleyball industry to evaluate the participation patterns in specialized volleyball training and implement changes to benefit specialized athletes and the industry. Keywords: sport specialization, participation, sport industry, volleyball
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".