Exploring the meta-transitions of first level elite athletes in the Chinese Whole Nation System: A polyphonic reflective tale
Bibliographic record
Abstract
We aim to advance athlete career scholarship using meta-transitions within the Chinese Whole Nation System (CWNS). The intentions are to: (a) provide in-depth understandings of Chinese elite athletes’ careers through shared dialog, (b) transform knowledge and support prospective athletes, and (c) contribute to the advancement of the CWNS by exploring its impact on elite athletes’ career development and transitions. A polyphonic reflective tale is introduced to extend reflective practice through amalgamating group reflections and represent our cultural reflections as former elite athletes. Group reflections were applied as a data collection method and a learning tool to increase self-awareness and understanding of our athletic careers. Our polyphonic reflective tale revealed barriers, resources, and thoughts through seven meta-transition vignettes, each a small turn relating to athletic and personal development, beginning with incipiency into elite table tennis (first level career) and spanning transitions into our post-sport careers: (a) the first level aspiration, (b) transition to the first level, (c) acclimation to the first level, (d) first level developments/improvements, (e) the first level trajectory, (f) post-first level career explorations, and (g) post-first level career developments. The polyphonic reflective tale is discussed as a career assistance strategy to support athletes’ careers.Lay summary: Comprehending how athletes’ meta-transitions can be facilitated within their sports environments will support athletes’ athletic and personal development. Our authors shared their personal experiences and reflections of their first level meta-transitions within the CWNS. The results emphasize the necessity of developing context-driven interventions during stages of athletes’ meta-transitions throughout their careers.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".