Bibliographic record
Abstract
It is only in equilibrium that the world will find peace. Charles de Gaulle For logically consistent realists, mutual deterrence games (or larger n -actor variants) are the only games in town. Realism, classical or neo-, loses much of its explanatory power if only some states are taken to be power maximizers, or if only some states are motivated by structural insecurity. As we have seen, however, logical consistency is not a hallmark of classical deterrence theory. Thus, conceptual and logical models of unilateral deterrence stand side by side in the strategic literature with models of mutual deterrence. Daniel Ellsberg's (1959: 358–359) critical risk model is a good example. In it, deterrence is seen as essentially a one-sided problem: how to deter a blackmailer, via threats, when the cost of executing the threat is prohibitive. But Ellsberg is not alone, and it is easy to understand why: the foundations of modern deterrence theory were laid against the backdrop of the Cold War. Most strategic thinkers of that era were understandably preoccupied with the question of how the Soviet Union might be deterred from attacking Western interests (and not vice versa). Thus, it should not be surprising that the assumption of asymmetry in offensive motivation figures prominently in the strategic literature. In fact, Jervis (1979: 297) reports that “most of the literature is written from the standpoint of the country resisting change.”
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".