Naming, labelling and the cultural construction of the identity of persons with albinism in East, Central, and West Africa
Bibliographic record
Abstract
Names and labels attributed to persons with albinism (PWA) in East, Central, and West Africa, reflect deeply rooted cultural beliefs, superstitions, and etiological and historical narratives. In these regions, the identity of the black PWA differs by his/her black/white duality – Black but not black, white but not White. Using Labelling and Symbolic Interactionism frameworks, we address three questions: (i) How are PWA named and labelled across East, Central, and West Africa? (ii) How can we categorise the predominant names and labels? (iii) How do these names and labels reflect socio-cultural conceptions, affect behaviour towards PWA, and contribute to the external construction of the identity of PWA? Principal categories emerge: supernatural affiliation, pigmentation, association with economic capital, vegetal and earth affiliations, zoosemic metaphors, and ambiguous/undetermined labels. These categories reflect cultural sentiments ranging from adulation to ostracisation, influencing social behaviour and identity construction. While some cultures regard PWA as celestial entities, others link them to curses or tragedy. Labels that commodify PWA render them targets for exploitation and violence, while vegetal, zoosemic, and ambiguous terminology contribute to alienation and identity crisis. This research demonstrates how cultural constructs of identity through language reinforce stigma and prejudice against PWA, worsening their social marginalisation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".