Bridging the Atrocity Prevention Gap between the International and the Local: Lessons Learned from Meso-Level Leadership in Ukraine and Syria
Bibliographic record
Abstract
This paper explores meso-level approaches to atrocity prevention that incorporate a wider array of local expertise at all stages, from early warning to transitional justice. Specifically, we treat meso-level approaches as encompassing local leadership and advocacy across bottom-up processes, grassroots efforts, victim-led activism, and civil society mobilization. We draw from two contemporary cases of mass atrocities, Ukraine and Syria, which exemplify such efforts, using primary interview data with meso-level actors. First, we examine atrocity risk early warning in Ukraine, where prescient local expertise at the meso-level failed to influence international responses, asking what went wrong and what more could be done to meaningfully incorporate their knowledge. Second, we explore how local efforts at justice-seeking in Syria filled accountability gaps left behind by failures at higher levels, as well as the lessons learned from attempts to localize transitional justice. We also present a framework developed to better learn and listen to meso-level expertise as a priority for the field of atrocity prevention. We conclude with practical recommendations for policymakers and practitioners working across early warning and transitional justice contexts.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".