Shipwrecks, Risk, and Social Constructions of Disaster, 1565–1761
Bibliographic record
Abstract
This paper considers shipwrecks as a form of disaster and studies historical shipwreck accounts to gain insights into social constructions of disaster. Theoretical debates address global inequalities in disaster experience, response, and research, and notably identify the concept of risk as a key to understanding social constructions of disaster. We analyze shipwrecks, a recurring but diverse risk, as a series of material and psychological stages that allow us to explore three facets of the social construction of shipwrecks. In the sixteenth century, Labrador whaling outfitters left shipwreck accounts that reveal how they converted the systemic risk of winter weather into an insurable natural hazard. In the seventeenth century, transatlantic fishermen blamed summer storms for their shipwreck losses, but also took responsibility as professionals for risk management and workplace security. In the eighteenth century, passengers’ accounts begin from an etic (observer) perspective and, at a certain stage of the shipwreck, shift to an emic (participant) perspective, enabling them to convert their experience into cultural capital within the emerging middle class. These facets of shipwreck accounts hold analogies for critical disaster studies, by revealing the role of social position in the perception, calculation, and cultural commodification of risk.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".