A Socio-Physical Approach to Systemic Risk Reduction in Emergency Response and Preparedness
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
This paper proposes a socio-physical approach that considers jointly the interaction and integration of the social and physical views of a system to improve emergency response and preparedness. This is accomplished through a reduction of systemic risk, which refers to a risk that could be greater than the sum of the risks of the individual system constituents. Using network analysis, it is shown that the explicit socio-physical approach yields meaningful qualitative and quantitative differences when compared with approaches that focus on the social and physical views in isolation. The benefits of this proposed approach are illustrated on a case study using clustering analysis and a proof-of-concept simulation. This new approach leads to systemic risk reduction by enabling a more informed and coordinated response strategy following an incident and a better identification of possible consequences and preparation strategies prior to an incident.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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