‘Skin Trade’: Genealogy of Anti-ageing ‘Whiteness Therapy’ in Colonial Medicine
Bibliographic record
Abstract
This article investigates the extent to which the emerging trend of do-it-yourself anti-ageing skin-whitening products represents a re-articulation of Western colonial concerns with environmental pollution and racial degeneracy into concern with gendered vulnerability. This emerging market is a multibillion dollar industry anchored in the USA, but expanding globally. Do-it-yourself anti-ageing skin-whitening products purport to address the needs of those looking to fight the visible signs of ageing, often promising to remove hyper-pigmented age spots from women's skin, and replace it with ageless skin, free from pigmentation. In order to contextualize the investigation of do-it-yourself anti-ageing skin-whitening practice and discourse, this article draws from the literature in colonial commodity culture, colonial tropical medicine, the contemporary anti-ageing discourse, and advertisements for anti-ageing skin-whitening products. First, it argues that the framing of the biomedicalization of ageing as a pigmentation problem caused by deteriorating environmental conditions and unhealthy lifestyle draws tacitly from European colonial concerns with the European body's susceptibility to tropical diseases, pigmentation disorders, and racial degeneration. Second, the article argues that the rise of do-it-yourself anti-ageing skin-whitening commodities that promise to whiten, brighten, and purify the ageing skin of women and frames the visible signs of ageing in terms of pigmentation pathology.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.031 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".