The Walkerton<b><i>E. coli</i></b>outbreak: a test of Rasmussen's framework for risk management in a dynamic society
Bibliographic record
Abstract
In May 2000, the water transportation system in Walkerton, Ontario (a small town with 4800 residents) became contaminated with E. coli bacteria, eventually causing seven people to die and 2300 to become sick. The 700-page report from a comprehensive public inquiry into this tragedy provided a rich source of data about the outbreak itself and the factors leading up to it. That report was used to test the explanatory adequacy of Rasmussen's framework for risk management in a dynamic society. Close agreement was observed between the predictions of the framework and the causes contributing to the Walkerton outbreak. The sequence of events reveals a complex interaction between all of the levels in a complex sociotechnical system spanning strictly physical factors, the unsafe practices of individual workers, inadequate oversight and enforcement by local government and a provincial regulatory agency and budget reductions imposed by the provincial government. Furthermore, the dynamic forces that led to the accident had been in place for some time—some going back 20 years—yet the feedback to reveal the safety implications of these forces was largely unavailable to the various actors in the system. Rasmussen's framework provides a theoretical basis for abstracting from the details of this particular incident, thereby highlighting generalizable lessons that might be used to ensure the safety of other complex sociotechnical systems.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| 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".