Empowering Women through TVET Training in Male Dominated Trades: A Project Supported by Canadian Embassy at Nakuru Training Institute Kenya
Bibliographic record
Abstract
Globally a wide gender gap has persisted over the years at all levels of Science, Technology, Engineering and Math (STEM) disciplines. Girls and women are systematically tracked away from science and math throughout their education, limiting their access, preparation and opportunities to go into these fields as adults. Women make up only 28% of the workforce in STEM. Men vastly outnumber women majoring in most STEM fields in college and in the market place. There is still a gross underrepresentation of women in the STEM fields in Sub-Saharan Africa (SSA) where the share of females graduating from tertiary education engineering fields is below 30%. The under-representation is a concern both for gender equality and economic competitiveness. // This study was based on Instructional Theory for Skills Development. It applied descriptive survey method. The study sample was 76 TVET female students, 36 for pre-training survey and 40 for post training survey. A gender based survey on the issues affecting women in the society, their employability and if young women would enroll in male dominated course given an opportunity was done. The project trained 40 women in technical skills for employability in two male dominated careers; electrical wireman and plumbing and pipe fittings. The 40 women were linked to industries for job related experience and were further registered for examination by National Industrial Training Authority (NITA) in Kenya. They recorded 100% pass rate and were certificated. 80% of the young women and girls are gainfully employed while 20% are pursuing further training. The study found out that young women are willing and are capable of training in skills in male dominated TVET sectors.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".