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Record W4408165232 · doi:10.22329/gljuh.v10i1.8656

Exploring Cosmetics as a Black Archive to Document the Experiences of Black Women

2025· article· en· W4408165232 on OpenAlexaffabout
Mya Trombley

Bibliographic record

Venue˜The œGreat Lakes journal of undergraduate history. · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicArchitecture, Design, and Social History
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsCosmeticsBlack womenGender studiesSociologyMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.732
Threshold uncertainty score0.733

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.052
GPT teacher head0.242
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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