Resource-Rich and Inclusive Vocational Education: Driving Entrepreneurial Intentions in Disadvantaged Student Populations
Bibliographic record
Abstract
Context: Vocational education has evolved from merely providing technical skills for employment to fostering self-employment and entrepreneurial careers, particularly in response to global unemployment challenges. This shift is critical in resource-constrained environments, where many youth face disadvantages due to cycles of generational poverty. While enhancing entrepreneurial intentions is now a goal within Technical Vocational Education and Training (TVET) institutes, it remains challenging in contexts where students are less resilient and more risk averse. Despite increasing efforts by vocational institutes to implement inclusive practices—such as providing diverse resources and accommodating various learning styles—little is known about the effectiveness of these initiatives in promoting entrepreneurial intentions. Approach: This study investigates the relationship between resource support mechanisms—including financial aid, access to technology, location assistance, and business mentorship—and inclusive teaching practices in relation to entrepreneurial intentions among disadvantaged TVET students in the Caribbean. Utilizing a quantitative design, data were collected from 240 TVET students via structured surveys. Findings: Findings reveal that inclusive teaching practices positively correlate with entrepreneurial intentions, while the four forms of resource support show no direct relationship. However, cluster analysis indicates that when both resource support and inclusive teaching are perceived as high, entrepreneurial intentions also increase. Conclusions: The results underscore the importance of a comprehensive support system that combines resource provision with inclusive teaching to benefit disadvantaged populations. Policymakers and TVET administrators should prioritize synergistic strategies to enhance entrepreneurial intentions, paving the way for improved opportunities and outcomes for marginalized students.
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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 | 0.004 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".