Targeted Killings: Assessing the Legal Contours for Protection of Africans with Albinism
Bibliographic record
Abstract
Abstract As the world mourns and condemns the brutal death of George Floyd by police brutality, one cannot ignore, but reflect on similar atrocities committed against hundreds of Africans by virtue of their skin pigmentation. These victims are Africans with albinism (AwA). Widespread discrimination and targeted attacks against these individuals occur against the backdrop of an erroneous mythology that the body parts of AwA have magic powers which could enhance electoral victory, guarantee bumper harvest, cure medical complications and bring riches. Indeed, many states have attempted to avert further abuse by arresting and prosecuting perpetrators. Yet, violations abound. By June 2020, more than 200 Africans with albinism have been killed in 30 African countries, and a disproportionate percentage has been subjected to abduction, rape and violent attacks. These atrocities question the effectiveness of existing (inter)national human rights mechanisms in safeguarding vulnerable populations from their attackers. Coincidentally, the day June 13 is significant as it marks the 5th Anniversary of the International Albinism Awareness Day (IAAD). While reminding us to combat different forms of discrimination faced by AwA, the day also starkly entreats us to survey the level of legal safeguard afforded this vulnerable community in the region. In a bid to forestall further attacks, the paper argues that while it may be vital for the international community to adopt an overarching binding legal instrument speaking to the protection of AwA, African countries should use the IAAD to reinforce information dissemination and awareness campaigns to destigmatize albinism in local communities.
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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 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".