Bibliographic record
Abstract
How the Weak Win Wars: A Theory of Asymmetric Conflict. By Ivan Arreguin-Toft. New York: Cambridge University Press, 2005. 250p. $75.00, cloth, $29.99 paper. The question of asymmetric conflicts or, more precisely, wars between two states of unequal power capabilities is an important one, but it has received scant scholarly focus, especially in the international relations field. More importantly, the subject of weaker actors winning wars against stronger adversaries has received limited attention. This is especially puzzling since during the Cold War, both superpowers experienced defeat or stalemate at the hands of weaker powers. In the case of the Soviet Union, an ill-fated asymmetric war in Afghanistan contributed to its demise as a state. America's failure in Vietnam had a major impact on U.S. domestic politics and foreign policy behavior for years to come. It affected American strategy regarding war in the developing world, encouraging the development of and reliance on new precision-guided weapons systems and strategies that would preclude ground combat. The failure of France in Indochina and Algeria also point to the significance of the phenomenon of asymmetric war. The Israeli and American withdrawals from Lebanon in 1982 and 1983 and India's pulling out from Sri Lanka in 1990 are other instances of stronger powers failing to make gains against their weaker adversaries. In the post-9/11 world, asymmetric conflicts have increasingly received the attention of military strategists as a result of the wars in Afghanistan and Iraq, but they have not received commensurate attention from IR scholars.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".