Pathways to green careers: using MICMAC analysis to address gender barriers in STEM-related TVET education in Colombia
Bibliographic record
Abstract
Abstract Women’s participation in Green-STEM (Science Technology Engineering and Mathematics) careers remains low. This study examined how to remove gender barriers in Environmental-STEM technical and vocational training programs, aiming to expand opportunities for women in the green economy. SENA, Colombia’s public TVET institution, served as the case study. Gender Transformative and Participatory Action Research approaches, along with the MICMAC method, were used to identify, analyze, and address key barriers. Surveys, interviews and focus groups provided qualitative and quantitative data. The MICMAC analysis revealed the relationships among barriers and their interdependencies, identifying nine core barriers. To address these, women-led smart strategies were implemented through learning cycles, supported by small- and full-scale green pilots focused on sustainable resource utilization, production patterns, and circular economy knowledge transfer. As a result, female apprentices strengthened their sustainability-focused skills and confidence, while SENA enhanced its capacity to foster more inclusive Green-STEM vocational pathways. This study expands existing knowledge by deepening the understanding of gender barriers in vocational STEM careers related to sustainability and environmental management in Latin America, where research remains Limited. It offers actionable recommendations on leveraging education to drive progress toward SDGs 4, 5, 12, and 13.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".