Employability Requirements in the Labour Market: Analysis of Advertised Job Vacancies in Ghana
Bibliographic record
Abstract
Background: Employability is often broadly defined as an individual’s ability to gain employment, to maintain employment or to replace an employment relationship by another. This study seeks to provide information on employability demands in the Ghanaian labour market. Method: The study is based on an in-depth analysis of job advertisements in the most widely read national daily, the Daily Graphic Newspaper. Analysis of the study was done using IBM-SPSS version 25. Results: More than half of all advertised jobs (54.3%) were for Professionals and Management Officials. About 22.8% of all advertised jobs were open to persons with no academic qualifications. Of the remaining 77.2% that required educational qualifications, almost half (47.3%) were open to university first degree holders. Job seekers who lack job-specific skills, computer literacy and communicative skills are not likely to succeed in the Ghanaian labour market. Also from the results, one may secure a job from age 25 and is most likely to secure a suitable job by age 35 with a minimum of 3 years of working experience. However, the likelihood of securing a job reduces as one approaches age 45. Conclusion: The study concludes that training and preparation for the job market should begin early enough for all prospective job seekers. Also, persons undertaking higher learning should take advantage of any small period of time in internship programs, voluntary works and industrial attachments to acquire the necessary work experiences needed to be competitive in the search for jobs in the Ghanaian labour market.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".