The 1628 Vasa Inquest in Sweden: Learning Contemporary Lessons for Effective Death Investigation.
Bibliographic record
Abstract
Much that is constructive can be achieved from analysis of death investigations that have failed to achieve desirable outcomes in terms of learning lessons about risks to health and safety and in terms of gaining an understanding as to how further tragedies can be avoided. This article reviews an "inquest" into the sinking in 1628 of the pride of the Swedish Navy, the Vasa, and the factors that led to the inquest failing to come to grips with the various design, building, oversight, subcontracting, communication, and co-ordination flaws that contributed to the vessel being foreseeably unstable and thus unseaworthy. It argues that Reason's Swiss cheese analysis of systemic contributions to risk and modern principles of Anglo-Australasian-Canadian death investigation shed light on how a better investigation of the tragedy that cost 30 lives and a disastrous loss of a vessel of unparalleled cost to the Kingdom of Sweden could have led to more useful insights into the multifactorial causes of the sinking of the Vasa than were yielded by the inquest.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".