Employability skills of maritime business graduates: industry perspectives
Bibliographic record
Abstract
The maritime industry underpins international business and world trade. As to be expected, business management is critical for the maritime industry, requiring highly trained individuals and teams to lead the development, implementation and control of sound contemporary management practices. Maritime business degrees are developed by universities to meet such demand by providing graduates with sufficient skills for the onshore business-related roles. This empirical study conducted in Australia, USA and Canada, investigates current and future industry employability skills for maritime business graduates through focus groups, individual interviews and an online survey with senior managers in maritime organisations. This study found the important employability skills for maritime business graduates which include communication, problem solving, adaptability, self-management, team work, and digital literacy and technology. Demand for digital literacy and technology knowledge and skills have increased due to the maritime industry having a trend of moving towards digitalisation and automation. However, the survey findings revealed that a skills focus for maritime business graduates will not be the technology itself but the use and management of technology. In relation to future skills/knowledge required from maritime business degree graduates in 10 years’ time, communication and adaptability are recognised as being the most important. Employers expect that maritime business graduates should be able to adopt new technology and be competent in communication, and be more adaptable given the highly dynamic nature of the maritime industry. Moreover, they require graduates to be equipped with a higher level of computer skills, have a strong work ethic and multilingual skills.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.268 | 0.001 |
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 teacher head, 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".