Bibliographic record
Abstract
This report uses a generic two-stage escalation model to ask whether and when limited conflicts can occur. There are two players in the model: Challenger and Defender. Challenger can either initiate a conflict or not. If Challenger initiates, Defender can concede, respond-in-kind, or escalate. If Defender does not concede, Challenger can escalate. The process continues until one side concedes or both escalate. Limited conflicts do not occur in our model when information is complete or when Defender's threat to respond-in-kind is seen to be completely noncredible. They are also extremely unlikely when Defender is seen strictly to prefer a response-in-kind to immediate capitulation when challenged. Limited conflicts are most probable under a Constrained Limited-Response Equilibrium (CLRE). Constrained Limited-Response Equilibria only occur when there is uncertainty about Defender's willingness to respond-in-kind to an initiation. The conditions associated with the existence of a CLRE and the other equilibria of the model are illustrated, both graphically and via a numerical example. Typically, under a CLRE, Challenger initiates and Defender concedes. From time to time, however, Challenger misjudges Defender's intentions and is surprised by a limited response. At this point, Defender chooses not to escalate the conflict because it concludes that Defender will counter-escalate and an all-out conflict will occur. Real-life examples of this process include the Gulf War, the Cuban Missile Crisis, the Fashoda Crisis of 1898, and the Korean crisis of 1950.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 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".