Delegative peacebuilding: Explaining post-conflict selective violence
Bibliographic record
Abstract
What explains selective violence against social and community leaders in the aftermath of war? This form of violence is often regarded as a spill-over product of wartime violence. We argue, however, that post-conflict selective violence occurs when the state delegates peacebuilding responsibilities to local leaders in areas of state weakness. Delegative peacebuilding describes a state's efforts to involve social and community leaders in peace programs in areas where its security apparatus or bureaucracy is weak or nonfunctional. Delegative peacebuilding mobilizes community leaders to implement state-led peace initiatives, making them focal points in the redistribution of power and resources. This positioning challenges the entrenched interests of local elites and armed groups, who often perceive these leaders as threats to their status as beneficiaries of the wartime status quo, thereby increasing their exposure to targeted violence. Our argument is grounded in immersive field research in Colombia, which has seen soaring levels of selective violence following the 2016 Peace Agreement. Employing a difference-in-differences design, we test the theory with original data on the assassination of social and community leaders (2014–2020).
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".