Exploration of the contribution of maritime education and training to the growth of the maritime industry : a case study of Cameroon
Bibliographic record
Abstract
of Cameroon Degree: Master of Science The aspiration of sustainable development requires us to resolve common problems and tensions and to recognize new horizons.Education must find ways of responding to such challenges, taking into account multiple worldviews and alternative knowledge systems, as well as new frontiers such as advances in digital technologies.In the maritime industry, it is difficult for education and training alone to solve all development challenges.A holistic approach to education can and should contribute to achieving a new development model.It requires a perfect operation and flexibility of a system in the complex environment of various challenges, growth prospects notwithstanding.The essence of MET in the maritime industry is immeasurable.Therefore, MET as a system can only succeed in its drive by opening up to the other stakeholders in its environment.The maritime industry is an international industry that is growing very rapidly.Its human resource needs are enormous.METIs thus exist to empower these personnel with the necessary skills and competences.The exploration of the contribution of MET to the growth of the maritime industry therefore seeks the challenges, possible solutions and prospects of MET in this direction.A mixed method of data collection and analysis is used in the research.The 33 respondents to the closed and open-ended questions in the survey, revealed perceptions of poor and inadequate use of modern technology tools, high degree of corruption, poor educational policies, insufficient lecturers in METIs, amongst others as challenges to both METIs and the shipping companies.As a system, there is a need for total cooperation to eliminate the challenges and forward to the future.v However, some limitations encountered include the limitation of sample population size, poor communications due to poor and lack of internet facilities, inadequate time to stretch to a wider range of interviews for greater reliability of the research outcomes.These did not negate in any way the results of the findings due to the diverse and rich educational and professional backgrounds of the respondents.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".