Bibliographic record
Abstract
There are two factions in a conflict. A third-party may choose to intervene by supporting one of the factions. We consider a third-party who maximizes a weighted sum of the welfare of the warring factions and the non-combatant population. In the case of a nonmilitary intervention, we obtain the following results: if the third-party cares equally about the warring factions and the rest of the population, then he will not intervene. If the third-party cares more about the warring factions, then he might intervene and will help the stronger faction unless he places a sufficiently higher weight on the welfare of the weaker faction. The stronger faction is able to appropriate more resources from the rest of the population. However, we find that helping the stronger faction might make the rest of the population better off, since this reduces the aggregate cost of conflict. On efficiency grounds, helping the weaker faction is optimal if success by the weaker faction preserves the rule of law, respect for private property leading to higher output. We also find that the third party is likely to intervene if success in the conflict is extremely sensitive to effort. In the case of military intervention, we find that the third-party will intervene if he cares sufficiently about the rest of the population or cares about the net resources that will be left after the war. We present examples where the third-party chooses military intervention over non-military intervention and vice-versa.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 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".