Research on Innovation and Entrepreneurship Leadership of Chinese Higher Vocational Students
Bibliographic record
Abstract
The purpose of this research is to evaluate the core competence of Chinese vocational college students' innovation and entrepreneurship leadership. According to the evaluation results, the researchers analyzed the causes of the existing problems, and put forward some suggestions for the reform of the teaching model of innovation and entrepreneurship education in higher vocational colleges in China. In this research, 287 sophomores from Guangdong College of Hotel Management in the autumn semester of 2024 are taken as samples. The Self-assessment Scale of Chinese College Students' Innovation and Entrepreneurship Ability is used as an evaluation tool, and the evaluation methods such as mean value and standard difference are adopted to obtain the comprehensive evaluation results of Chinese vocational students. The research results show that many qualities of Chinese vocational college students are in line with their age characteristics, such as self-confidence, perseverance, and ideas, but their theoretical knowledge and ability of innovation and entrepreneurship leadership need to be improved. This further shows that the traditional teaching model is no longer suitable for innovation and entrepreneurship education. After analyzing the evaluation results, the researchers believe that Timmons' entrepreneurial process theory and integrated leadership theory should be used as the theoretical basis of innovation and entrepreneurship education teaching model, and put into practice in innovation and entrepreneurship projects and competitions, so as to improve students' innovation and entrepreneurship leadership.
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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 | 0.001 | 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) | 0.002 | 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".