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 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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".