The Development of Sustainable Training for Youth Dancesport in China
Bibliographic record
Abstract
This research aims to study factors related to sustainable training for youth dancesport in China. The second aim is to study mediating roles of motivation to learn and expectation fulfillment with sustainable training for youth dancesport in China. The third aim of the study is to improve sustainable training for youth dancesports in China. The fourth aim is to study and provide long-term training for youth dancesport training in China. This study examines the youth dancesports training market in the training service industry. The 346 teachers from 10 dancesport training institutions in 8 cities in China were selected as the sample size. Data collection using questionnaires and interviews was designed according to reliability and validity, mean, standard deviation, factor analysis, and Structural Equation Model (SEM). The research found that learning motivation has a positive effect on transfer motivation and training migration, expectation fulfillment has a positive effect on motivation transfer and training migration. Transfer motivation and training migration have a positive effect on the sustainability of dance training. Transfer motivation mediates the sustainability of learning motivation on dance training. Training migration mediates the sustainability of expectancy realization of dance training. For the mediating roles, training transfer has a mediating role in learning motivation on the sustainable development of dance training satisfaction. Transfer motivation has a mediating role in expectations to achieve the sustainable development of satisfaction with dance training. Training transfer is expected to mediate the sustainable development of dance training satisfaction. The study showed SDGs impacted the degree of Chinese youth’s sustainable dancesport training on sustainable development.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".