Theoretical Perspectives on Learning for Prevention of Fishing Vessel Accidents
Bibliographic record
Abstract
Fishing vessels come to grief partly because theory pertaining to accidents and their prevention is unduly Functionalist and obsessed with technical matters (involving equipment). The author applies to fishing accidents a social cartography that emphasizes the importance of power relations and ontology. Four paradigms—functionalism, humanism, radical humanism and radical functionalism—are used to raise issues pertaining to accidents and their prevention. The utility of the cartography is demonstrated by revisiting accident reports concerning Scotia Cape, a large Canadian vessel that disappeared with the loss of seven lives. At the centre of this analysis is the need to broaden prevention programs so as to have adequate regard to human factors and the political economy of the fishing industry. Résumé L'auteur soutient que les bateaux de pêche sont impliqués dans des accidents, partiellement parce que la théorie au sujet des accidents et de leur prévention est excessivement fonctionnaliste et met l'accent sur les aspects techniques, comme l'équipement. Il applique aux accidents de pêche une «cartographie sociale» qui insiste sur l'importance des relations de pouvoir et de l'ontologie. Quatre paradigmes; le fonctionnalisme, l'humanise, l'humanisme radical et le fonctionnalisme radical sont utilisés afin d'analyser le problème des accidents de bateaux et leur prevention. L'utilité de la cartographic proposée est démontrée en réexaminant les rapports d'accidents de Scotia Cape, un grand navire canadien qui a disparu avec sept personnes à bord. L'élément central de la présente analyse réside dans le besoin d'élargir les programmes de prévention afin de mieux tenir compte du facteur humain et de l'économie politique de l'industrie des pêches.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".