A test of Rasmussen's risk management framework in the food safety domain: BSE in the UK
Bibliographic record
Abstract
In 1986, bovine spongiform encephalopathy (BSE) was identified in the UK. Millions of BSE-infected cows were slaughtered and over 150 people contracted variant Creutzfeldt–Jakob disease, an inevitably fatal human form of BSE. Tragic incidents such as this provide valuable opportunities to understand and improve the safety of complex socio-technical systems. By studying accidents, knowledge can be gained that can improve system safety. The purpose of this article is to test the usefulness of Rasmussen's risk management framework for explaining how and why accidents occur in the food production domain. This was accomplished by using the framework to retrospectively investigate how and why BSE was transmitted through the human and animal food supply in the UK from 1986 to 1996. More specifically, an AcciMap and Conflict Map were constructed to represent contributing factors of the epidemic according to the structure of Rasmussen's framework. These factors were used to test the seven predictions made by the framework. All seven predictions were supported by the evidence, indicating that Rasmussen's risk management framework shows promise as a theoretically driven explanation of how and why accidents happen in complex socio-technical systems, particularly in the food production domain.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".