Tackling the anarchy within: the role of deterrence and great power intervention in peace operations
Bibliographic record
Abstract
My dissertation strives to understand the conditions under which peaceoperations in intra-state wars succeed or fail. I address two main questions: Whatis peace operation success, and what contributes to such an outcome? I define thesuccess of a peace operation based on two dimensions: a) the accomplishment ofthe peace operation's mandate, and b) the establishment of order. This definitionallows me to avoid a binary framework of assessment in terms of success vs.failure by introducing intermediate categories: partial failure and partial success.To explain peace operations' outcomes, I look at the role of the type of strategyadopted and the type of intervener. I suggest that the three major ingredients ofany strategy are: communication, capacity and knowledge. These ingredients allinteract differently depending on which strategy is adopted. I apply my theoreticalframework to empirical cases, testing the saliency of my postulates by examining11 peace operations in three countries: Somalia (1991-1995), Sierra Leone (1999-2005) and Liberia (1990-2009). I assess these operations' outcomes and theprocesses by which they succeeded/failed at accomplishing their mandate whilesimultaneously contributing/hindering their chances at re-establishing order. Iargue that, for a peace operation in an intra-state war, the adoption of a deterrencestrategy works best for re-establishing order while the involvement of a greatpower facilitates the accomplishment of the mandate.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".