Generic Skills of Prospective Graduates from the Employers’ Perspectives
Bibliographic record
Abstract
Past studies on employability of graduates have placed great emphasis on the supply side efforts in generic skills development which includes the tertiary curriculum design and delivery mechanisms. However, the responsibility of employers in providing training to prospective graduates and collaborating with universities in enhancing generic skills has been raised. On the demand side, there are numerous studies that have examined employer’s perspective in the private sector but few studies have examined employer’s perspective in the public sector. The objectives in this study are twofold: (1) to identify employers’ perception of the ideal generic skills that graduate employees should possess, and (2) to elicit employers’ perception of the lack of generic skills that prospective graduates (i.e. industrial trainees) currently possess. A qualitative research design was utilized, involving primary interview data collected through 16 key informant interviews of employers in the public sector in Kuantan and Johor Bahru. These key informant employers were selected from the UKM’s social science industrial trainees who attended training at these two sites. These interview data were analyzed using content analysis. The findings indicate that there are specific generic skills in the area of information and social interaction skills that the public sector employers seek from the graduates. This study implies the need for a stakeholder-responsibility approach in prescribing a comprehensive normative solution to the employability of graduates. In addition, it also postulates that the culture of learning and gaining varied skills in different spheres of life need to be inculcated amongst students from early years.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".