Readability of maritime accident reports: a comparative analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it