Competencies for fresh graduates’ success at work: Perspectives of employers
Bibliographic record
Abstract
This article investigates Hong Kong employers’ views on graduate competencies that facilitate new graduates’ success in the workplace. The methodology involves the use of a questionnaire to elicit responses from business employers on the importance of specific competencies contributing to the success of fresh graduates at work. The findings indicate that all of the competencies examined are important to a degree. ‘Ability and willingness to learn’, ‘teamwork and cooperation’, ‘hardworking and willingness to take on extra work’, ‘self-control’ and ‘analytical thinking’ are the five highest-ranking competencies measured, although all are clearly necessary for success. Hard and soft skills are rated equally important by employers overall. Recommendations for developing competencies among university students prior to their entry to the workforce are discussed. As the competencies are of a practical nature, it is suggested that universities work together with industry to develop workplace-oriented programmes. This is the first research, to the authors’ knowledge, that approaches desirable graduate competencies from the perspective of the skills gap in the context of Hong Kong.
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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 | 0.004 | 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 | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| 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".