Terrorism Risk Assessment, Recollection Bias, and Public Support for Counterterrorism Policy and Spending
Bibliographic record
Abstract
Recollection bias (RB) refers to the phenomenon whereby after an adverse event people report that their risk assessment about a similar future event is presently no higher than their recollection of their pre-event risk assessment. While previous research has outlined this theoretical construct and generated important empirical findings, there were some limitations. We design and employ a new national representative survey to address these limitations in this study. We examine the existence and persistence of RB among the general public in the context of a number of domestic and international terrorist attacks. We further examine the socioeconomic and political base of RB and the influences of RB on a wide range of citizens' counterterrorism policy preferences. Our data analyses reveal strong evidence showing the occurrence of RB and its persistence across various forms of terrorism risk. With regard to the socioeconomic and political base, we find that females, older people, political conservatives, and Republicans are less likely to be subject to RB. For the effects of RB on public counterterrorism policy preferences, our analyses demonstrate that this bias significantly dampens public support for a wide range of preventive policy measures and government anti-terrorism spending. Overall, our study, based on a national representative sample and an extended survey design, provides robust evidence of RB in terrorism risk assessment, and adds further evidence to support the idea that RB is likely a generalizable phenomenon. Implications and suggestions for future research are discussed in the conclusion.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".