Development of Strategies for Sustainable Development of Chinese Dance Teacher Leadership in Shandong Province
Bibliographic record
Abstract
The objectives of this research were 1) to study the current situation and supporting factors that enhance the sustainable development of Chinese dance teacher leadership in Shandong Province, 2) to develop the strategies for sustainable development of Chinese dance teacher leadership in Shandong Province, and 3) Evaluate the feasibility of the strategies for sustainable development of Chinese dance teacher leadership in Shandong Province. The sample group of this research consisted of 331 teachers for questionnaires and 12 experts for interview who worked for Chinese Dance in Shandong Province and were sampled through random clusters. The research instruments included 1) questionnaires, 2) interviews, and 3) evaluation forms. The data analysis statistics were percentages, mean, standard deviations, and content analysis. The results revealed the following: 1) The current situation and supporting factors that enhance the sustainable development of Chinese dance teacher leadership were moderate. 2) The strategies for enhancing the sustainable development of Chinese dance teacher leadership include 7 aspects: Professional quality, organizational mechanism, evaluation system, resource support, teacher training, discipline development plan, and international exchange and cooperation. The adaptability and feasibility evaluation results of the strategies implementation were high at the highest level.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".