Bibliographic record
Abstract
Agreements wherein parties pledge various forms of military cooperation are common in the international system and are often prominent in conflict settings. In 1949, for example, the United States, Canada, and numerous Western European states formed the North Atlantic Treaty Organization (NATO) to counter the threat of attack by the Soviet Union. In turn, the Soviet Union and various Central and Eastern European nations formed the Warsaw Pact in 1955 to counter threats from NATO. Since the end of the Cold War, the Warsaw Pact has dissolved while NATO has remained in force and expanded its membership. Article 5 of the NATO Charter specifies that an attack against any member state will be considered an attack against all member states. Following al Qaeda's attack against the United States on September 11, 2001, Article 5 was invoked for the first time. The United States and its NATO allies then cooperated in an invasion of Afghanistan to overthrow the Taliban, who had supported al Qaeda. US-led forces also cooperated militarily with the “Northern Alliance,” an intrastate coalition of Afghan groups fighting against the Taliban. Meanwhile, various transnational terrorist groups and criminal syndicates have been reported to cooperate with al Qaeda in ways that appear to constitute non-state alliance behavior. The scholarly literature on alliances is vast, encompassing both theoretical and empirical research, with extensive coverage of political and economic determinants and effects. In this chapter we focus primarily on economic aspects of military alliances.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.073 | 0.030 |
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".