Bibliographic record
Abstract
Albinism is a rare genetic condition that affects the pigmentation of the retina, hair and skin. Consequently, people with albinism world-wide experience the stigma and negative repercussions of an unconventional physical appearance, as well as a visual impairment. The medical literature has focused extensively on the genetics of albinism amongst animals, but it has been relatively under-studied and ignored in sociology. People with albinism have rarely had the opportunity to tell their stories; to tell their sorrows and their triumphs. This paper attempts to remedy this failure in social science. In-depth interviews were conducted with seven women and five men, living in various countries globally. The study is framed around Erving Goffman's theory of stigma and 'spoiled identity', as well as the more recent Disability Studies that stresses 'the normals' as being the 'identity spoilers' or the 'problem'. The participants revealed victimisation from various sources including students, teachers, employers, colleagues, strangers and the medical profession. Focus is placed on the strategies that respondents have devised in coping with these adversities. The results identify eight principal methods of reaction and response to the discrimination against people with albinism. These eight different strategies of resistance to the stigmatisation of albinism are essential elements of personal change and even, possibly, social change. This paper quotes respondents' own words. Such methodology offers the chance for people with albinism to voice their experiences, and for us researchers to listen and learn.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.018 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".