Employment Outcomes Following Industrial Attachment in Kenya
Bibliographic record
Abstract
Abstract Unemployment is a one of the main concerns for youth in Kenya (Hope, 2012). One of the programs put in place to address this concern is Technical and Vocational Education and Training (TVET) programs. These programs teach students technical trades and place them in industrial attachment programs in order to increase their employment opportunities and social mobility (Ohangwu et al., 2022). This study focuses on one TVET institution in particular: Tumaini Innovation Center in Eldoret, Kenya. This school offers certifications for street-connected youth in six different technical trades while also teaching classes in localized engineering education, entrepreneurship, life skills, and information and communication technology (Tumaini Innovation Center | Because Together We Can, n.d.). The purpose of this study is to determine the factors influencing a student's employment outcome following graduation from a technical training program. Interviews were utilized as the main form of data collection and were conducted with current students, alumni, employers, and faculty from the Tumaini Innovation Center. The interviews focused on past and future attachment and employment experiences, connection to, assistance from, and experience with Tumaini, and dreams for future employment. Quantitative demographic data was also collected from students and alumni. The interview data was then analyzed using a mixed methods approach where trends were examined within individual sample groups and compared across groups. From this analysis, a framework was created describing students' pathways following graduation. This framework outlines five main paths that students take. Upon completion of their industrial attachment, students may be employed by their attachment provider, gain employment from another company, pursue higher education, start their own business, or end up unemployed. The reasons for taking these pathways vary and will be discussed in more detail in the paper. Regardless of which pathway students ended up on, overwhelmingly positive experiences were reported at Tumaini, and students benefited socially, emotionally, and professionally. This paper contributes new perspectives on the potential for technical training and industrial attachment programs to address unemployment issues in Kenya and may inform similar challenges in other contexts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".