Shifting Wrongs to Rights: Lessons in Human Rights from the Situation of Mothers Impacted by Albinism in Africa
Bibliographic record
Abstract
Abstract Debates about legitimizing human rights in Africa have centred on making universal human rights principles relevant to local social and cultural contexts. Localizing human rights norms requires seeing human rights in terms of relevance to specific situations rather than as the application of abstract principles. In this paper, scholars and advocates analyse the challenges in the practice of human rights, with a focus on mothers impacted by albinism, whether as mothers of children with albinism or as mothers with albinism themselves. Women and girls impacted by albinism are particularly vulnerable to human rights violations and reflect the unfulfilled promise of the United Nations principle to ‘Leave no one behind’. On account of intersecting factors—including denial of humanity; gendered stigma, discrimination and disenfranchisement; lack of access to the social determinants of health; and violence—mothers impacted by albinism are truly amongst those ‘furthest behind’. Drawing on the frameworks of vernacularization and culturalization, we conceptualize and contextualize human rights in relation to the unique experiences of mothers impacted by albinism in Tanzania, South Africa, and Ghana. Our analysis takes up four particularities that pose challenges to protecting the rights of mothers impacted by albinism: personhood as foundation for human rights; the communal nature of human rights; proportionality and human rights; and the intersectional nature of human rights. These particularities shed light on human rights practice for mothers impacted by albinism and carry implications for human rights practice more broadly.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.041 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".