‘Killing Your Way to Victory’: The Failure of the Kill/Capture Strategy Against al Qaeda
Bibliographic record
Abstract
The strategy of either killing or capturing al Qaeda cadres today stands as the dominant United States counter-terrorism strategy. This strategy, however, has failed to destroy al Qaeda, and has instead expanded the organization's political ideology into a major force being felt throughout the Middle East. Kill/Capture's appeal stems from assessments of al Qaeda as a vast network, articulated best by scholars such as Peter Bergan and Bruce Hoffman. The strategy also has appeal from several historical examples, and the early cost-effective successes found in Kill/Capture's implementation immediately after the September 11th attacks. Yet these advantages are outweighed by the strategy's strengthening of al Qaeda's brand among other groups, the indiscriminate nature of the strategy, and its inability to offer other political solutions versus al Qaeda's ideology within the context of violence and conflict. As a result, al Qaeda has endured, while expanding its ideology across the Middle East. Militant Takfirism today, is now largely defined by al Qaeda's ideology, and is best seen with the current situation in Iraq and Syria. Hence, while Kill/Capture offers some credible appeal, the strategy has failed overall to rid the world of al Qaeda.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".