Tourism and terrorism The determinants of destination resilience and the implications for destination image
Bibliographic record
Abstract
Safety is essential in order for a destination to maintain and increase tourism activities (Gupta et al., 2010; Hall et al., 2004). In comparison, terrorist attacks are more likely to have negative effects on tourism than natural disasters (Sönmez et al., 1999). During the last decades, several terrorist acts have been committed in touristic cities of the North and South (including Boston, Istanbul, Manchester, New Delhi, New York, Paris, and Tunis). Security concerns and the threat of violence perpetrated by certain groups with radical political and religious demands do not only affect a destination’s image and reputation and individual decisions about whether to visit a given destination. They also influence the political and economic balance, which in turn affects the environment in which the tourism industry operates (Hall et al., 2004). While some destinations appear to be suffering the long-term consequences of terrorist attacks on their tourism industry (Liu and Pratt, 2017), others are successfully keeping their industry afloat and avoiding significant economic downturns (Gurtner, 2007; Putra and Hitchcock, 2006). We are therefore seeking to understand the reasons why some destinations manage to maintain their image and remain attractive to tourists despite terrorist acts and others struggle to overcome the consequences of such acts on their industry, even years after the fact.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".