Readability of maritime accident reports: a comparative analysis
Bibliographic record
Abstract
Maritime accident reporting is performed as a means for experience feedback within and across organizations. While the quality and representativeness of the findings are critical to prevent similar accidents from occurring in the future, various contextual factors concerning the reports can affect the ability of various actors to use these effectively as a basis for learning and action. Research suggests that the readability of safety documents is essential to their successful adoption and use. However, there is currently no empirical knowledge about the readability of maritime accident reports. Consequently, this study presents a comparative analysis of quantitative readability metrics of maritime accident reports. Three-year data extracted from reports by five English-language national accident investigation authorities, and one industry reporting system are used. The results show that the language used is commonly at the post-secondary reading level. Reports by the Nautical Institute’s Mariners’ Alerting and Reporting Scheme are written at a high school level and thus easier to read. Statistical variation of readability of reports by different organizations is significant. Implications for future research and practice are discussed. The main recommendation for reporting organizations is to be mindful of language complexity and simplify where possible.
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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 | 0.011 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".