"Have you got it?" Overcoming the futility of training mariners about collision regulations
Bibliographic record
Abstract
Despite aids to navigation, there are too many collisions involving deep-sea ships, tugs, workboats, fishing vessels, and recreational boats. Nautical trainers claim to teach collision regulations (COLREGS), but training has little to do with learning. Most mariners have only a surface (or no) understanding of maritime "rules of the road." In British Columbia, collisions have caused multiple fatalities and traumatized rescuers. Lecturing is not a good way to prevent collisions at sea. The British Columbia Institute of Technology's Marine Campus is committed to "excellence in training." The author reflects on his experience as a student there and suggests the institution adopt less authoritarian and more participatory, engaging, and respectful ways of fostering learning. Teaching COLREGS is as much a theoretical as a practical problem. Marine institutes should back away from training, put less focus on teaching and more on having people learn together. Résumé Malgré les aides à la navigation, il y a trop de collisions impliquant les navires de haute mer, remorqueurs, bateaux de travail, de pêche et les bateaux de plaisance. Les formateurs nautiques prétendent quʼils enseignent les règlements pour prévenir les abordages (COLREGS), mais la formation a peu à voir avec lʼapprentissage. La plupart des gens de mer n’ont quʼune compréhension en surface (ou même aucune compréhension) des “règles de la route” maritimes. En Colombie-Britannique, les collisions ont causé plusieurs morts ainsi que de nombreux sauveteurs traumatisés. Le cours magistral nʼ’est pas la bonne façon de prévenir les collisions en mer. Le campus marin de l’Institut de Technology de la Colombie-Britannique sʼest engagé à offrir une formation dʼexcellence. Lʼauteur reflète sur son expérience en tant quʼ’étudiant auprès de lʼInstitut et suggère à lʼInstitut dʼadopter des méthodes moins autoritaires, plus participatives, engageantes et respectueuses afin de favoriser un meilleur apprentissage. Lʼenseignement des règlements pour prévenir les abordages est un problème théorique autant que pratique. Les instituts maritimes devraient prendre du recul face à la formation, placer moins dʼ’importance sur lʼenseignement et mettre plutôt lʼemphase sur le fait que les gens doivent apprendre ensemble.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".