Maritime Undergraduate Students: Career Expectations and Choices
Bibliographic record
Abstract
The following study presents an inquiry into understanding the motivating factors of students to enroll in a maritime university and to further continue with a career in the maritime industry. By collecting data from 378 undergraduate students enrolled in various maritime programs (navigation, electromechanics, electrical engineering, and economic engineering in transport), we aimed to better understand the profiles of students, their information sources, interest, and the prospects of associated programs where they are enrolled. As such, this study seeks to enable educators and industry practitioners to better understand the educational and career paths chosen by undergraduates in the maritime field. It can align the students’ expectations with program delivery. We examined students’ perceptions and assessments according to the program they are enrolled in. Taking into account the fact that there is a world low attraction for maritime careers, the results of this study are useful for maritime education and training (MET) providers during the design and marketing campaign of the educational program to attract students. Additionally, the findings are useful for public administration and the Ministry of Education’s analysis of expanding educational and research programs, as well as for Ministry of Labour forecasting. Employers from the maritime industry can find useful the main motives for which a graduate would choose to work in this sector, business field, or a related business. Moreover, industry practitioners and academia can expand the study at a larger scale, comprising more countries and taking into account national and regional characteristics.
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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 | 0.005 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".