Preventing shipping accidents: Past, present, and future of waterway risk management with Baltic Sea focus
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
Various national maritime authorities and international organizations show strong interest to implement risk management processes to decision making for shipping accident prevention in waterway areas. There is a recurring need for approaches, models, and tools for identifying, analysing, and evaluating risks of shipping accidents, and for strategies for preventively managing these in (inter-)organizational settings. This article presents a comprehensive review of academic work in this research area, aiming to identify patterns, trends, and gaps, serving as a guide for future research and development, with a particular focus on the Baltic Sea Region. To understand the links between research in the Baltic Sea area and the global community, a bibliometric analysis is performed, focusing on identifying dominant narratives and social networks in the research community. Articles from the Baltic Sea area are subsequently analysed more in-depth, addressing issues like the nature of the academic work done, the risk management processes involved, and the underlying accident theories. From the results, patterns in the historical evolution of the research domain are detected, and insights about current trends gained, which are used to identify future avenues for research.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it