Risk Perception, Threat, and Anxiety Decay in Lone-Wolf Terrorist Events in the US
Bibliographic record
Abstract
This study, Risk Perception, Threat, and Anxiety Decay in Lone-Wolf Terrorist Events in the US, was conducted by researchers at the Institute for Science, Technology and Public Policy and funded by the National Science Foundation, Grant Award 1624296. The study consisted of a two wave panel survey designed to provide increased knowledge about the US public's understanding, attitudes, risk perceptions, and policy preferences concerning lone-wolf terrorist attacks, allow comparison of such characteristics to those the public holds towards organized terrorist attacks, track decay or amplification of risk perceptions over the duration of the study, and test the theory of recollection bias. The first wave of the panel (May 2016) measured multiple characteristics associated with perceptions of various types of terrorism attacks, especially lone-wolf attacks. The second wave measured the same characteristics about six months later (November 2016), enabling the researchers to assess changes over time and in relation to additional violent incidents that occurred between the first and second wave. Project Team: Kent E. Portney - PI; Jeryl Mumpower and Arnold Vedlitz - Co-PIs; Xinsheng Liu, Bryce Hannibal, and Carol Goldsmith - Senior Investigators
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".