A Content Analysis of Celebrity-Endorsed Cosmetic Products in Fashion Magazines
Bibliographic record
Abstract
While there have been many studies examining celebrity-endorsed advertisements, few have studied the relationship between race and ethnicity and body types found in celebrity-endorsed advertisements. A sample of 265 celebrity-endorsed advertisements was collected from both Cosmopolitan and ELLE magazine during the first quarter of the year (January, February, March) between 2008-2013. A content analysis was constructed in order to determine the race and ethnicity of each celebrity found in the advertisement as well as their body type. The results suggest that there has been a tendency to use non-minority celebrities in celebrity-endorsed advertisements than non-minority celebrities. Furthermore, the findings demonstrate that most of the celebrities used within the advertisements closely identified with an ectomorph body type, which suggests that the majority of celebrity models used in celebrity-endorsed advertisements will have a thin and slender body type. The results of the study confirm that there has not been an increase in the prevalence of diverse celebrities, in terms of race and ethnicity, in these advertisements through 2008-2013 and that there is no relationship between race and ethnicity and body type.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".