Integrating Ethnic Craft Skills into the Fine Arts Pedagogy at Chinese Higher Vocational Institutions in Guizhou Province
Bibliographic record
Abstract
This study aims 1. To explore the roles of teachers, students, and inheritors in integrating Guizhou ethnic craft skills into vocational art education, 2. To investigate the contributions of theoretical cognition, practical ability, and innovative development to improving the integration of Guizhou ethnic craft skills into fine arts education. The quantitative data showed that teachers, students and, inheritors, pedagogy-based theoretical cognition, practical ability, and innovative development all had a significant impact on the integration of ethnic skills into the teaching and learning of art education in higher vocational colleges and universities in Guizhou, with practical ability being a key issue in the integration of ethnic skills into the teaching and learning practice of higher vocational art education. The results of the study presented that 1) the significant positive correlations with the influence of the inheritor are, in order of ranking, teaching practice with the integration of ethnic skills, teaching design with the integration of ethnic craft skills, cultural heritage with the integration of ethnic skills, skill mastery with the integration of ethnic skills, and teaching evaluation with the integration of ethnic skills, 2) the theoretical cognition, practical ability, and innovative development would have a significant positive influence on the integration of ethnic craft skills into the teaching practice of higher vocational art education.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".