Mass Atrocities in Ethiopia and Myanmar: The Case for ‘Harm Mitigation’ in R2P Implementation
Bibliographic record
Abstract
Abstract By combining insights from the three dominant perspectives in International Relations – liberalism, realism, and anti-imperialism – a novel approach is put forward, that of ‘harm mitigation’. A comparative analysis of Ethiopia and Myanmar reveals that the international community still does not possess the mechanisms to halt mass atrocities in real time. When enforcing R2P, none of the available non-coercive and coercive policy options are pragmatically or ethically unassailable. The non-coercive tools that can be labelled as ‘ethical’, such as diplomacy, humanitarian assistance, and documenting atrocities, while important, are largely ineffective at stopping atrocities as they happen. Much like UN peacekeeping, these non-coercive actions are limited by targeted governments invoking the principle of state sovereignty. Meanwhile, actions that are potentially expedient, such as economic sanctions, military intervention, and supporting rebel groups, are ethically thorny. The conclusions speak to the reality that both non-intervention and intervention have the potential to cause human suffering.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".