Maritime English Learning Materials Based on Standard Training Certification and Watchkeeping for Seafarers (STCW) Curriculum and Intercultural Competence (IC)
Bibliographic record
Abstract
The objectives of this study is to investigate 1) the current existing conditions of Maritime English learning materials of Deck Department of Maritime Education and Training (MET), 2) the Maritime English learning materials needed by the students of Deck Department of Maritime Education and Training (MET), and 3) the intercultural competences needed by the students. This study is conducted by qualitative method. The respondents of the research are the active students, lecturers, active and retired seafarers, cadets, port authority, and administration staff. They are taken by purposive random sampling. The data are collected by questionnaire, documentary sheet, and in depth interview. The data are analyzed by descriptive qualitative. The results of the study show that 1) generally the existing Maritime English Materials mostly are not in line with the STCW’10 and need to be developed in order to meet the minimum standard of Standard Training Certifications and Watchkeeping for Seafarers (STCW’2010), 2) there are various topics of Maritime English learning materials needed by the students in terms of both English language and content or professional subjects, 3) and the various contents of intercultural competences are also needed by the students before they go to work at sea in order to effectively communicate with other crew of different cultural background.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".