The Politics of Hostage Rescue: Is Violence a Route to Political Success?
Bibliographic record
Abstract
Over the years, law enforcement agencies have acquired extensive experience with hostage incidents, and most Western countries have officers trained in all aspects of hostage resolution. There are also articles and manuals outlining how to deal with the media coverage of hostage takings (Scanlon, 1989). However, because hostage rescue efforts can provide dramatic visuals that attract enormous audiences, the media have steadily intensified their coverage of such incidents. Today, a group of previously obscure persons can suddenly dominate the media agenda by successfully resisting an armed assault or by seizing hostages and calling themselves terrorists. After defining a hostage incident and looking at the strategy for dealing with such incidents, this article examines the implications of two fatal incidents: the stand‐off involving religious fanatics at Waco, Texas; and the Air France hijacking that started in Algiers and ended in Marseille, France. Both became number one on the Western media agenda, and both became political crises involving the head of state; one threatening a president’s credibility, the other enhancing a president’s status. Together they suggest that the escalating media coverage of such incidents raises questions not only about the effectiveness of current response strategies, but also about political leadership. This article discusses a number of strategies that have been tried or suggested. It also debates whether involvement has a positive or negative effect on political leaders. It concludes that, from the evidence available, a successful hostage rescue can yield political rewards.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".