How to Win a Genocide Case: Analyzing the Triple Strategy of the Advocates of the Rohingya in Myanmar
Bibliographic record
Abstract
The Rohingya Muslim minority in Myanmar was subjected to discrimination and gross violations of human rights for many decades. During the last two waves of military crackdowns in Rakhine State (October 2016 to January 2017; August to September 2017), the Tatmadaw army and civilians committed atrocities against the Rohingya that amounted to crimes against humanity and genocide. Advocates for the Rohingya's suffering took action to leverage the findings of the investigations of international mechanisms. They endeavored for an international condemnation of Myanmar at the ICJ, and they filed a complaint in an Argentinian court for the application of universal jurisdiction to prosecute the military and the political leadership responsible for ordering and committing the atrocities. They also encouraged an investigation of the atrocities in the ICC. The litigators’ main focus was set on genocide. However, while genocide carries the stigma of being the most heinous of crimes, it is also the hardest to prove, particularly the special intent to commit it. This article assesses the chances of the triple strategy applied by the Rohingya advocates. It argues that litigating the case in three different fora, assures that the forums back each other up, so that the flaws of one are compensated by the others. Thus, the chances for accountability for the crime of genocide are increased. The fora work interoperably to achieve the goal of proving the occurrence of genocide in Myanmar so as to impose state responsibility and individual criminal responsibility.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.007 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".