Africa’s Youth Unemployment Challenge and the Pursuit of Soft Skills Development by University Students
Bibliographic record
Abstract
This paper seeks to address the growing challenge of youth employment in Kenya. The study explores how the provision and acquisition of soft skills by university students influence their employability in the current labour market in Kenya. The objective of the paper is to examine the current training programs in soft skills development being offered to university students and the extent to which they enhance the ability of the participants to obtain employment. We use a case study approach to ascertain the opportunities provided by the Employment Training Program which offers mentoring and coaching to young people in key soft and employment skills as they transition from tertiary institutions into the workforce. The paper triangulates quantitative and qualitative methodologies that draw on a pre-training survey, key informant interviews, a post training survey and focus group discussions to inform the study. The research shows that addressing the information gap for job opportunities can help reduce youth unemployment. The development of entrepreneurship, interpersonal skills, public relations and online jobs search skills are amongst the observed training gaps. The findings of the study further indicate that employers are interested in young people who are consistent, reliable, have good communication and presentation skills as well as realistic career expectations. The study concludes that whilst several students seem keen on obtaining soft skills, some are unable to capitalize on the acquisition of such skills to enhance their employment prospects. The paper recommends incorporating employability programs into the Kenyan educational curriculum at the secondary and tertiary levels to address the vicious cycle of unemployment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".