Twisting One Arm: The Effects of Biased Interveners
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There is currently little consensus on whether impartiality is necessary for the successful management of today's conflicts, especially when using forceful intervention. In this article we identify how combatants will react to forceful biased intervention by a third party based on the extant models of conflict and intervention. Under fairly general circumstances the favoured side is expected to de-escalate, while the targeted side is expected to escalate, in response to such a forceful intervention. Two case studies, NATO's intervention in Kosovo, and India's intervention in Sri Lanka, are then interpreted in the context of the model. While the cases highlight areas where the models and hypotheses need to be refined, they provide preliminary support for the arguments derived from them. We conclude with policy implications and argue that formal modelling can be a useful tool in trying to specify the conditions under which a biased forceful intervention is likely to either inhibit violence or act as a catalyst to it.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it