Strategies for Promoting Sustainable Employability Development Among Students in Higher Vocational Colleges
Bibliographic record
Abstract
How could higher vocational colleges improve students' sustainable development employability? This paper takes 384 students (192 liberal arts students and 192 science students) from ten higher vocational colleges in Guangdong Province as the research subjects and explores how higher vocational colleges can improve students' sustainable development employment ability. The study designed questionnaires grounded in the Career EDGE model and specifically analyzed students' capabilities in 8 areas, which included career development learning, experience (work and life), degree subject knowledge understanding and skills, emotional intelligence, general skills, self-efficacy, self-confidence, and sustainable development. It also delved into the existing issues. The findings indicated that while students demonstrated strengths in degree subject knowledge and emotional intelligence, they encountered challenges in career development learning and sustainable development skills. To bridge these gaps, the study utilized a SWOT-PEST analysis and proposed a suite of targeted strategies. These strategies encompassed integrating practical training programs with academic curricula, nurturing self-efficacy through mentorship initiatives, bolstering school-industry collaboration, and crafting a personalized career planning framework. These strategies provided vocational colleges with actionable approaches to help prepare students for a changing labor market and sustainable career paths.
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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 | 0.003 |
| 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 | 0.004 |
| 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".