Understanding Public Responses to Domestic Threats
Bibliographic record
Abstract
The overall goal of this report is to improve understanding of public responses to domestic threats. Project 1 focuses on pandemic influenza and dirty bomb threats, aiming to understand the role of emotions in anticipated behavioral responses. Project 2 examines a situation in which people are evacuated from a community to avoid exposure to radioactive fallout from an upwind nuclear explosion. This project aims to understand the factors that affect people's decisions about how long to wait until returning to their homes, given the gradual decline in radiation levels resulting from radioactive decay. First, the authors present an overview of each problem using models that summarize scientific knowledge. The models use logic of influence diagrams with nodes that reflect relevant variables affecting risk, and mitigating it, and links showing how they are connected. The models differ from traditional risk models because they include emotional and behavioral components that affect how a risk event unfolds. The Project 1 models focus on the interplay between emotional and behavioral responses to domestic threats, particularly fear and anger. The model for Project 2 focuses on the health, social, and economic factors that may affect people's decision to return to a community with residual radiation levels that elevate cancer risk. Second, they report on surveys of Canadian and U.S. participants based on these models. For Project 1, they found that, independent of anger and trait emotions, fear was related to seeing more risk of morbidity and mortality, and predicting less resilience, more compliance with mitigation strategies, and higher likelihood of being absent from work in the case of pandemic influenza. For Project 2, they found that people's decision to return were affected by the cancer risk of radioactive fallout as well as the availability of free housing in the evacuation zone.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".