Exploring Cosmetics as a Black Archive to Document the Experiences of Black Women
Bibliographic record
Abstract
This paper examines the uses, methods, strengths, and weaknesses of cosmetics as a historical archive to better understand the experiences of Black women in North America during the twentieth century. Encompassing cosmetic products, advertisements, magazines, and companies, the archive of cosmetics documents Black women’s secondary role within the cosmetic industry as both buyers and businesswomen. As large, white-owned cosmetic companies virtually dominated the makeup industry, Black women were sidelined and racially exploited, largely rendered unable to exercise the agency to determine their own beauty standards through dictating the representation of, messaging around, and distribution of beauty products designed for Black women. Their lack of agency within the cosmetic industry will be explored through the beginning of the twentieth century, during which the emphasis on skin lightening products reinforced the undesirability of dark skin. Throughout the later twentieth century, despite the emergence of the ‘Black is Beautiful’ campaign, there was instead a predominately superficial shift in the cosmetic industry with companies releasing new lines specifically designed to suit dark skin. Moreover, the experiences of Black women in the United States and Canada will be analysed through the use of case studies of Madame C.J. Walker and Viola Desmond to further contextualise and compare the exclusion of Black women within the cosmetic industry across North America.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.033 | 0.019 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".