Developing a simple yet rigorous approach for operational risk management for small vessels
Bibliographic record
Abstract
Fishing is seen as one of the most dangerous occupations in the world, and the people affected by the accidents at sea are often among the poorest in the society as found by the International Labor Organization (ILO). About 95% of fishers worldwide are small scale fishers and it is estimated that as much as 40% of the global landings comes from small scale fisheries according to recent studies conducted by the Food and Agricultural Organization (FAO), in partnerships with Duke University and WorldFish. Some studies have in the past documented fishing accidents and spelt out various hazards and consequences relating to outcomes including injury, vessel damage and loss, and death. There is, however, limited information regarding national and global ranking of these hazards and consequences to help identify the patterns associated with the risk, and hence target training resources in the direction of most probable occurrences is difficult. It is therefore essential to study and assess the interactions among the influential risk factors and the management strategies that can be employed to mitigate their impacts and improve training. This research work seeks to study and develop a simple but rigorous operational risk modelling and management approach for small vessels that are used in fishing and transportation. A comprehensive probabilistic analysis was required to propose a simple applicable method to analyze risk causal factors of small fishing vessel operations. This was followed by the development of an operational risk model for small fishing vessels. The model was further analyzed with expert data along with secondary data from literature using a hybrid quantitative model for operational risk. In completing the research study, a case for an operational risk management approach for small fishing vessel is proposed using the cost per unit risk reduction (CURR) model to select a risk control option. Several small fishing vessel accidental events were attributed to operator error, vessel factors and environmental factors. Based on the findings of the research it is recommended that a combination of administrative and personal protective equipment control measures be adopted by the stakeholders.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".