Employability Distress: Factors Affecting ICT Graduates’ Employability and Work Integration in the Workspace
Bibliographic record
Abstract
There has been an ongoing debate on the subject of graduate unemployability in South Africa and the extent to which it has affected academic graduates. The growing consensus regarding intensifying levels of graduate employability distress today is explicitly supported theoretically in a number of research studies. This is despite the considerable efforts that have historically been made concerning the graduate labour force and their probable ramifications in the context of South Africa’s far-reaching labour market issues. A number of scholars both in the past and the present have identified several issues emerging from the heterogeneity of the quality of education, graduates’ capabilities and skills competencies, higher education institutions’ involvement in the industries and employers’ requirements for employment. This study aimed to explore the views that graduates conceive about employability and stress, reason for their unemployment and identify the factor that causes them to be unemployed and decisively suggest way to address these challenges. A quantitative approach and a questionnaire were used. A total of 195 questionnaires from graduates/students in the College of Business and Economics were recovered and usable. The findings reveal a paradox in the increased level of ICT graduate unemployment and skills mismatch and shortages. This has been attributed mostly to organisational changes which may have given rise to misalignment between the skills that graduates currently have and those that employers seek from graduates. Findings suggest that the kind of education obtained, graduates’ incapability to apply the skills received and work experience are factors hindering employability.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".