Bibliographic record
Abstract
A rational strategy for the employment of nuclear weapons is a contradiction in terms. Robert Jervis The consensus view of classical deterrence theory is deficient, both empirically and logically. Empirically, the theory is hard put to explain, inter alia , the stability of the Cold War period before the Soviet Union achieved essential equivalence with the United States, the absence of an all-out conflict between the Soviet Union and China, especially during the most contentious stretches of this strategic relationship, and the historical tendency of major-power wars to occur under parity conditions. Logically, the theory is marred by a fundamental incompatibility between its tenets and the canons of rationality. This is the paradox of mutual deterrence . More specifically, logic implies that the status quo should unravel as higher and higher costs render mutual conflict worse and worse for both sides. Classical deterrence theory, however, asserts the opposite. States clearly do not always behave the way classical deterrence theory suggests they do or should. Waltz (1993: 53–54) notwithstanding, the glaring discrepancy between logic and fact, between prescription and description, is troubling. Accordingly, in this chapter and the next, we inquire whether classical deterrence theory can be resuscitated, that is, whether it can be rendered logically coherent and, ultimately, empirically accurate. To this end, we now explore the merits of three potential resolutions of the paradox of mutual deterrence that attempt to explain general deterrence stability without violating the rationality postulate: deterministic threats, “threats-that-leave-something-to-chance,” and a solution suggested by the theory of metagames.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".