Relentless Atrocities: the Persecution of Hazaras
Bibliographic record
Abstract
As one of the main ethnic groups in Afghanistan, Hazaras are Farsi-speaking and mostly Shi’a Muslims in a predominantly Sunni Muslim country. They are also distinguishable by their Asiatic appearance. Throughout Afghanistan’s history, Hazaras have suffered considerably under different regimes, enduring recurring massacres, enslavement, and forced displacement. Despite Afghanistan’s accession to the Rome Statute in 2003, the plight of Hazaras has not improved. Indeed, the assaults on Hazaras have only intensified in recent years, impacting virtually every aspect of their lives. This article argues that the recent and ongoing attacks against Hazaras constitute a crime against humanity. In particular, I show, element by element, that there is a reasonable basis to believe that the assaults on Hazaras amount to persecution based on ethnic and religious grounds pursuant to article 7(1)(h) of the Rome Statute. Accordingly, the International Criminal Court and the global community must take urgent actions to investigate the relentless atrocities against Hazaras and to hold the perpetrators accountable. Failure to do so, as warned by the U.S. Holocaust Memorial Museum, may lead to a full-blown genocide.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.023 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".