Bibliographic record
Abstract
Equilibrium was the name of the game. Henry Kissinger The decision to escalate … is a strategic issue, involving not only assessment of the immediate advantage to one's own side, but also difficult and often painfully uncertain calculation of the possibilities for counterescalation by the enemy. Richard Smoke By design, the deterrence models explored in part II are extremely austere. To be sure, the “no-fat” modeling approach we adopt allows us to focus directly on the role of uncertainty and credibility in both mutual and unilateral deterrence games. But axiomatic austerity cuts both ways. The ability to penetrate core theoretical structures and analyze the role of a few fundamental variables is not altogether costless. Parsimony is inversely related to the complexity and range of questions that a model can fruitfully address. For example, in the simple models developed in part II, conflict is an all-or-nothing proposition. As a consequence, these models are unable to shed any light on the conditions associated with either limited conflicts or escalation spirals. Nor do our rudimentary models capture well the subtleties of some more complex deterrence situations. Thus, to address these and related limitations, we now begin to complicate, ever so slightly, our bare-bones deterrence models and to explore a number of questions associated specifically with extended deterrence relationships. In this chapter we begin by describing a generic two-level extended deterrence/escalation model and discuss its characteristics under complete information.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".