Exploration and Practice of Integrating the Shangshan Culture into Art and Design Teaching in China’s Higher Vocational Colleges
Bibliographic record
Abstract
Archaeological authorities consider Shangshan culture, a unique name for China’s outstanding local culture, to be the origin of the world’s painted pottery civilization, Chinese farming villages, and the world’s rice civilization. Shangshan culture is more than 3,000 years old compared to the local civilization discovered by historians. We hypothesized that introducing art and design majors into China’s vocational colleges is an effective method for promoting local culture. Integrating local culture into art and design teaching in China’s vocational colleges provides diversified teaching resources, heightens students’ sense of cultural identity, improves their creative performance, and extends local culture’s influence. Therefore, by analyzing the current status of art and design teaching in China’s vocational colleges, we highlighted local culture’s role in the teaching process. We adopted a quasi-experimental design method to conduct an experiment on 90 students from a vocational college majoring in an art and design course. These students learned about the integration of Shangshan culture into art and design under their teachers’ guidance and applied their learning to their artistic creations. This study’s results will help better integrate local culture into art and design teaching, and consequently, achieve a win-win outcome.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".