Exploration and Prospects on Development Trends of “People First” and “Entertainment” of Chinese Gymnastics-In Changzhou Canadian Inspire Sports Gymnastics Club as Case
Bibliographic record
Abstract
In the process of National Fitness wave and socio-economic development,most of Chinese sports items play their due role in the mass sports and building a harmonious society,and their market-oriented and industrial development have yet entered high-speed development stages.But as advantage events in the world competitions,gymnastics is currently facing with bad situations which workout population is lack,mass base is weak,fitness effect is not satisfactory.As Canada's national famous gymnastics club,Inspire Sports Gymnastics Club has accumulated rich experience and reputation in their domestic market management and education,it enters the Chinese market,combining with China's national situations,excavating the secrets and exploring the key points of gymnastics giving interest and funny to the people.And it is committed to the popularity and promotion of gymnastics in China,complying with the future market development and the trend of gymnastics development and promotion.As an example,we explore and research the way of Chinese gymnastics People First and entertainment development,which will have important reference value to the Chinese gymnastics market-oriented development,expanding population gymnastics,mining and exploring the gymnastics' roles to mass sports fitness.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".