Contradictions in the practices of training for and assessment of competency
Bibliographic record
Abstract
Purpose The purpose of this paper is to highlight the contradictions in the current maritime education and training system (MET), which is based on competency‐based education, training and assessment, and to theorize the failure to make the training useful. Design/methodology/approach A case study of education and training in the international maritime domain was conducted. Data sources include historical documents, rules and regulations concerning MET, syllabi, handouts, sample questions, field notes, an ethnographic study in a maritime college and interviews conducted with experienced mariners and course lecturer. Findings There are contradictions in the education and training system that do not allow the targeted objectives to be fulfilled. Fundamentally, the assessment system has changed the objectives of the education and training practices from learning skills and knowledge required on‐board ships to passing competency examinations. Practical implications The practical implication of this research is valuable for the International Maritime Organization, marine administration and maritime training institutes to think over the competency‐based system in practice today and how to improve the present maritime training and assessment system in order to achieve its authentic objectives. Originality/value This research identified and bridged the gap in literature and research of competency‐based training and assessment in the maritime domain and provides practical solutions for improving this system.
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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 | 0.076 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.035 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".