Investigation of the preparedness of maritime education and training institutions (METIs) of seafarer’s top supplying countries in the introduction of the maritime autonomous surface ship (MASS)
Bibliographic record
Abstract
Looking back on the history of the shipping industry, seafarer's competency evolved with the technology onboard ship.To address the problem of safety, security and protecting the environment, the Maritime Autonomous Surface Ship (MASS) revolutionized the shipping industry.Seafarer's functions will be replaced by machines that require new competency of seafarers to man the automated ships. This paper aims to investigate the preparedness of Maritime Education andTraining Institutions (METIs) of top supplying countries of seafarers in providing the adequate number of seafarers with required competency for automated ships.Systems Theory was used to identify the factors affecting the METI's preparedness in providing the required competency for seafarers in the introduction of MASS.Mix-method aids the researcher to have a deeper understanding of how the METIs function as a system and how the factors for preparedness affects the METIs in implementing the required competency of seafarers for MASS by comparing for validity and reliability complementing both the qualitative and quantitative data.The investigation revealed that investing in resources without the regulatory framework is a waste of time and money due to uncertainties of future requirements in implementing the required competency of seafarers for MASS.In conclusion, respondent countries are waiting for the approval of regulatory framework and are not making any preparations for MASS but it can be observed that from the hierarchy as top suppliers of seafarers going down, their strategy on how to remain relevant in the future depends on their level in the hierarchy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".