Pathways to green careers: using MICMAC analysis to address gender barriers in STEM-related TVET education in Colombia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it