Required and Possessed University Graduate Employability Skills: Perceptions of the Nigerian Employers
Bibliographic record
Abstract
University is a place where skilled labour is produced for societal and global consumption. This is premised on thefact that education provided at this level enhances human capital development which widens employmentopportunities. However, there seems to be disparity over the skills required and those possessed by graduates fromNigerian universities. As a result, many university graduates are either underemployed or unemployed. The studyadopted descriptive survey research design. Using stratified and simple random sampling techniques, a total of 300employers of labour drawn from the manufacturing, banking and finance, education, and telecommunicationindustries in Lagos State constituted the sample frame. A Skill Assessment Questionnaire (SKAQ) was designed toelicit information from the participants. The results obtained showed that skills required of university graduates asperceived by employers were analytic and problem solving (98%), decision – making (98.3%), risk management(96.7%), leadership (98%), information and communication (97.7%), team-work (99%), official communication(97.7), and English proficiency and literacy skills (97%) while skills possessed by university graduates were Englishproficiency and literacy (58%) and information and communication skills (53%). These results showed disparity inboth the employers required skills and those possessed by the university graduates. The study, therefore,recommends that Nigerian university curriculum should be revised to reflect courses that will teach the required skillby employers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".