Explaining Third‐Party Intervention in Ethnic Conflict: Theory and Evidence
Bibliographic record
Abstract
Abstract.One of the most challenging developments for students of international relations is the resurgence of ethnic strife, including secessionism and irredentism. Basic questions are only beginning to be addressed in the post‐Cold War era. Why are some states more likely than others to intervene in ethnic conflicts? How can international norms about third‐party intervention in ethnic conflicts be evaded or ignored by some states but respected by others? Why are some states inclined to use force rather than mediation to resolve ethnic strife? In short, what accounts for the emergence of adventurous and belligerent foreign policies with respect to internal ethnic conflicts? These questions are of increasing importance to students of international politics, yet the dynamics and internationalisation of ethnic conflict are far from fully understood. This study focuses on the dynamics of third‐party intervention in ethnic strife and implications for peaceful resolution. The first section presents a model that identifies the general conditions under which ethnic strife is most likely to lead to intervention by third‐party states. The second uses four cases to illustrate, within the context of the model, different processes with respect to internationalisation of ethnic conflict. The third and final stage identifies implications for policy and theory, along with directions for future research.
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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 | 0.022 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 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".