Fishing Vessel Safety in Indonesia: A Study of Accident Characteristics and Prevention Strategies
Bibliographic record
Abstract
Fishing is a crucial economic activity in Indonesia, supporting millions of people's livelihoods and food security.However, it is also one of the most hazardous occupations, exposing workers to various risks of accidents, injuries, and fatalities.Fishing vessel accidents can severely affect crew members, vessels, and marine ecosystems, resulting in human losses, economic damage, environmental impacts, and social problems.This study aims to analyze the types, locations, and causes of fishing vessel accidents in Indonesia, using data from various sources, such as official reports, maritime authorities, and news articles.The results show that the most common types of accidents are drowning, burning, and injury or death of ship crew.The most frequent locations of accidents are the Java Sea and the Malacca Strait.The main causes of accidents are human error, weather conditions, technical factors, and environmental factors.The study concludes that fishing vessel safety is a complex issue that requires a comprehensive and collaborative approach involving various stakeholders.The study also suggests possible solutions to improve fishing vessel safety, such as improved design and construction standards, enhanced weather forecasting and warning services, effective safety management systems, and behaviour change interventions.This study contributes to the literature on fishing vessel accidents and provides valuable insights for policymakers, stakeholders, and researchers in Indonesia.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".