A Social‐Cognitive Perspective of Terrorism Risk Perception and Individual Response in Canada
Bibliographic record
Abstract
The volume of research on terrorism has increased since the events of September 11, 2001. However, efforts to develop a contextualized model incorporating cognitive, social-contextual, and affective factors as predictors of individual responses to this threat have been limited. Therefore, the aim of this study was to evaluate a series of hypotheses drawn from such a model that was generated from a series of interviews with members of the Canadian public. Data of a national survey on perceived chemical, biological, radiological, nuclear, and explosives (CBRNE) terrorism threat and preparedness were analyzed. Results demonstrated that worry and behavioral responses to terrorism, such as individual preparedness, information seeking, and avoidance behaviors, were each a function of cognitive and social-contextual factors. As an affective response, worry about terrorism independently contributed to the prediction of behavioral responses above and beyond cognitive and social-contextual factors, and partially mediated the relationships of some of these factors with behavioral responses. Perceived coping efficacy emerged as the cognitive factor associated with the most favorable response to terrorism. Hence, findings highlight the importance of fostering a sense of coping efficacy to the effectiveness of strategies aimed at improving individual preparedness for terrorism.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".