Behind the headlines? An analysis of accident investigation reports
Bibliographic record
Abstract
This paper reports on an analysis of 319 accident investigation reports published over a ten-year period by four maritime authorities. In doing so it highlights the immediate and contributory causes identified by the report authors and aggregates these to create an impression of the major causes of accidents as identified by investigators over a decade. The aggregation and analysis suggest that non-seafarer related factors constitute more than one quarter of all the causes identified in the reports. In particular, third party deficiencies, poor design, and technical failure are prominently identified as causes of ‘fire and explosion’ and ‘lifeboat’ accidents. In ‘grounding’ and ‘collision, close quarter & contact’ accidents, causes such as ‘poor judgement/operation’, ‘failure in communication/coordination’, and ineffective/inappropriate use of technology stand out. Of greatest overall concern to accident investigators was ‘inadequate risk management' and 'failure in communication' despite the implementation of the ISM Code. In addition to the aggregate analysis presented, the paper offers illustrative examples from specific accident investigation reports whilst acknowledging the complexities of accident causation and the dangers of oversimplification in the assignation of accident cause.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".