Applying developmental model of sport participation to South Korea's sport context: Student athletes' early retirement approach
Bibliographic record
Abstract
Over 90 percent of basketball student athletes in South Korea retire early due to physical, social, or psychological challenges. after they do so, they may struggle to adapt to their school lives and to live the rest of their lives as non-student athletes. In light of these challenges, elite and recreational sports organizations which had operated separately for several decades were merged in 2015 in order to create a more supportive environment for young athletes. The purpose of this study was to explore students' experience with early retirement and to assess the initiatives planned by Korea's newly integrated sports organizations using a psychological-based model, the Developmental Model of Sport Participation(Côté, Baker, & Abernethy, 2007). Four early-retired student athletes and two employees at sports organizations dedicated to student basketball were recruited to participate in semi-structured interviews. To increase credibility, this study utilized techniques including member checking, investigator triangulation, and data source triangulation. Early-retired student basketball athletes stated that they felt negative emotions such as sorrow, anger, and regret about their early retirement at the time they left basketball. In addition, the DMSP is closely related to the new integrated organizations' objectives, it could therefore positively influence students' experiences of early retirement. The Korean sports system has provided only one model for athlete development: early specialization, which may cause physical, social, or psychological harm. Therefore, the DMSP could be an effective guideline for Korea's new sports organization and may help it to create a new pathway for students to become elite basketball athletes. It could thus alleviate the negative implications of early retirement among Korean student athletes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".