Understanding the meta-transition experiences of Chinese table tennis athletes during the transition to first level teams: Group reflective practice interviews
Bibliographic record
Abstract
BACKGROUND: Researchers have highlighted elite and professional athletes' demands and barriers during their broader transitions within national sport systems from Western perspectives. Using a culturally sensitive methodology, we aimed to broaden this topic by exploring the elite athletes' meta-transitions and experiences during the transition to first level teams within the Chinese sport context from an Eastern philosophy/perspective. METHOD: Data collection was conducted through five group reflective practice interviews, aligning with our Eastern philosophy and scientist-practitioner positions. Fourteen elite male table tennis athletes in first level teams from eight provinces in China participated. The interviews were analyzed through a template analysis which is a form of qualitative analysis used to interpret participants' meta-transitions and experiences through a double hermeneutic. The data is represented by a polyphonic tale to safeguard participants' anonymity whilst showcasing different demands and barriers as well as the relationships between each meta-transition from athletes' varying experiences. RESULTS: Five meta-transitions were found during Chinese athletes' transition to first level teams in the CWNS to describe their nuanced demands and barriers in each meta-transition. CONCLUSION: Culturally sensitive methodologies, the scientist-practitioner principles, and meta-transitions should be promoted to understand athletes' experiences and develop tailor career assistance and interventions that can help athletes strive for career excellence.
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".