Bibliographic record
Abstract
Do online shopping advertorials for whitening skincare products in South Korea perpetuate a racial hierarchy wherein whiteness is maintained as an ideal beauty standard? If so, how is this hierarchy articulated and reinforced with words and images? Whitening products, such as tone-up creams and sunscreens, have become increasingly prevalent in the skincare industry in South Korea. Adding a level of nuance to earlier research, my research undertakes a critical feminist discourse analysis method to examine 19 skincare advertorials on the South Korean beauty e-commerce site, Olive Young Global. This study breaks new ground by taking an inductive analysis approach to analyzing these advertorials to produce findings comparable to similar studies in other Asian countries. Thus, it works to confirm the overall message being communicated that these products are sold as the key to a woman’s quest for a white beauty ideal. By undertaking an inductive critical discourse analysis, the research will develop themes based on the exploration of these advertorials with some guidance from existing literature. The globalization of beauty promotes a falsely universal white(ned) woman, and this project evidences a nuanced analysis of the lexical choices and images employed to promote the idea that whiteness and youthfulness equate to “natural” beauty. This critical feminist discourse analysis will provide insight into how a racial hierarchy is reinforced through media and how the exclusion of racialized women from spaces intended to empower all women will reproduce the societal hierarchy among women within the beauty industry.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".