Travel Risks in a Time of Terror: Judgments and Choices
Bibliographic record
Abstract
Shortly after the 2002 terrorist attacks in Bali, readers of Conde Nast Traveler magazine were surveyed regarding their views on the risks of travel to various destinations. Their risk estimates were highest for Israel, and lowest for Canada. Estimates for the different destinations correlated positively with (1) one another, (2) concern over aspects of travel that can make one feel at risk (e.g., sticking out as an American), (3) worries about other travel problems (e.g., contracting an infectious disease), and (4) attitudes toward risk. Respondents' willingness to travel to a destination was predicted well by whether their estimate of its risk was above or below their general threshold for the acceptability of travel risks. Overall, the responses suggest orderly choices, based on highly uncertain judgments of risks. Worry played a significant role in these choices, even after controlling for cognitive considerations, thereby supporting the recently proposed "risk as feelings" hypothesis. Thus, even among people who have generally consistent and defensible beliefs, emotions may affect choices. These results emerged with people selected for their interest in and experience with the decision domain (travel), but challenged to incorporate a new concern (terror).
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".