Educational Management Strategies to Promote the Sustainable Development of Entrepreneurship of Students in Higher Vocational Colleges
Bibliographic record
Abstract
The This paper aims to explore educational management strategies for promoting the sustainable development of entrepreneurship among students in higher vocational colleges. The research subjects consist of 400 students from 10 higher vocational colleges in Guangdong Province. Based on the data analysis results from a questionnaire survey of these 400 students and interviews with 12 experts, it is concluded that the factors influencing the sustainable development of entrepreneurship among students in higher vocational colleges include entrepreneurship knowledge, entrepreneurship teams, entrepreneurial experience, entrepreneurial capital, and entrepreneurial skills. Additionally, the influencing environmental factors encompass the market environment, educational management environment, policy and legal environment, and family environment. Combining SWOT, PEST, and TOWS matrix analyses, this paper proposes educational management strategies to foster the sustainable development of entrepreneurship among students in higher vocational colleges. These strategies aim to cultivate students' entrepreneurial spirit, provide more entrepreneurial talents to society, and promote the sustainable development of the social economy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 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".