Prosecutorial-NGO Complex: new legal opportunity structures and the role of (I)NGOs in universal jurisdiction trials on Syria
Bibliographic record
Abstract
Abstract Changing legal environments create new opportunities for legal mobilization by civil society groups. At stake is mobilization in Germany and Europe for the prosecution of agents of the Syrian Assad regime accused of committing core international crimes. Changes in the legal environment include the (a) spread of universal jurisdiction; (b) increasing use of “crimes against humanity”; (c) new prosecutorial and policing units specialized in core international crimes; and (d) new prosecutorial practices, such as structural investigations. Coinciding with an influx of Syrian refugees, these opportunities give rise to a collaborative network of (I)NGOs that feed witnesses and evidence into prosecutorial agencies. Interaction between agencies and (I)NGOs contributes to the transnational ordering of criminal law and constitutes a Prosecutorial-NGO (P-NGO) Complex. (I)NGOs finally diffuse court narratives to a broad audience and shape public knowledge of grave violations of human rights. We focus on the P-NGO Complex for the al-Khatib universal jurisdiction trial before the Higher Regional Court in Koblenz, Germany. Empirical tools include an analysis of (I)NGO network structures and websites, interviews with court observers, activists, and prosecutorial staff, and an analysis of media reporting.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".