Limitations of the international commission of human rights experts in Ethiopia and its effects on the Oromo population
Bibliographic record
Abstract
The aim of this paper is to examine the limitations of the International Commission on Human Rights Experts on Ethiopia (ICHREE). Due to lack of sufficient resources, the ICHREE was not able to fully satisfy its mandate in “conducting thorough and impartial investigations into alleged violations and abuses of International Humanitarian Law (IHL) and International Human Rights Law (IHRL) committed by all parties to the conflict in Ethiopia since November of 2020” (Human Rights Council, 2022). This has resulted in catalogue of possible IHL and IHRL violations in Oromia to go unnoticed including the arbitrary deprivation of life by airstrike targeting civilian populations, extrajudicial killings, massacres and public executions. Also, the possible enforced disappearance and torture, cruel and inhuman treatment of Oromo Liberation Front (OLF) detainees. The lack of investigations into the Oromia region has also hindered avenues for accountability under international criminal law, specifically in terms of crimes against humanity. As avenues for accountability under Ethiopia’s domestic law lacks the criminalization of crimes against humanity, it imperative that the ICHREE provide independent and impartial investigations into the atrocities committed in the Oromia region as under domestic law they would be considered as “ordinary” offences. Ultimately, this will ensure accountability and justice for victims of possible serious violations of IHL and IHLR in the Oromia region and assist in transitional justice measures.
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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 | 0.055 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".