Gender-sensitive vocational and entrepreneurship education: addressing poverty for Caribbean women
Bibliographic record
Abstract
Gendered poverty remains a persistent issue in the Global South, particularly in the Caribbean, where economic activity heavily relies on volatile sectors such as tourism and agriculture, leaving many women unemployed and economically vulnerable. Despite investments in Technical Vocational Education and Training (TVET) to address unemployment, existing research inadequately explores how entrepreneurship education (EE) can be integrated into TVET to empower women. Women face distinct challenges, including entrenched gender stereotypes, systemic biases, and limited access to entrepreneurial opportunities, which further hinder their transition from skills training to sustainable self-employment. Adopting an exploratory research design, this perspective paper utilizes an integrated literature review method to synthesize insights from peer-reviewed studies, white papers, and policy documents, advocating for the integration of EE into TVET through a gender-sensitive approach. Two key findings emerged: (1) integrating EE within TVET can equip women with entrepreneurial skills that complement technical training, and (2) gender-sensitive practices, such as flexible curricula, gender sensitized modules, women-led business mentorship programs, women-only cohorts, and institutional support, are critical to achieving these outcomes. We conclude that this dual-focus model offers practical implications for TVET institutions to redesign their programs and collaborate with NPOs and policymakers to provide ongoing support for economically deprived women. By empowering women to transition into self-employment, the approach fosters inclusive economic growth, reduces poverty, and enhances social development. Furthermore, its broader adoption offers a pathway to addressing gender inequalities and promoting entrepreneurship development worldwide.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".