Mass Atrocities in Ethiopia and Myanmar: The Case for ‘Harm Mitigation’ in R2P Implementation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 it