Bibliographic record
Abstract
Abstract The existing literature offers contrasting views on the causes and effects of non-aggression pacts. Some scholars contend that these agreements impose audience costs that prevent an ongoing rivalry from escalating to war. Others claim that states use non-aggression pacts to signal to others that their rivalry is over and that their future relations will be peaceful. Scholars disagree as to the impact non-aggression pacts have on violent conflict. I demonstrate that various definitional and coding issues beset the literature, resulting in the incorporation of many agreements that should not be considered as non-aggression pacts. I then make a threefold argument about non-aggression pacts. First, non-aggression pacts came into being in the 1920s amid emerging norms proscribing interstate warfare. Second, they saw frequent use in interstate Europe. Nazi Germany and the Soviet Union used them to manipulate those norms so as to make themselves appear more acceptable despite their revisionism. Finally, many friendship treaties, which have been miscast as non-aggression pacts, are a separate type of agreement that became common among those post-colonial states that acquired independence during and immediately after the Cold War. Timeless arguments regarding non-aggression pacts thus reify these agreements and overlook key motives behind their use.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.000 |
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".