The Depiction of Beauty-by-Beauty Influencers on Instagram and Generations Z’s Perception of Them
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Social media influencers (SMIs) are immensely popular and act as cultural gatekeepers for beauty. While advertisers commonly believe that “beauty sells,” this study asks (1) what types of beauty do SMIs depict and how does it compare to that portrayed in fashion magazines over thirty years ago? (2) as cultural gatekeepers what cultural values do SMIs depict and how are they related to the types of beauty? And (3) what are Generation Z’s (Gen Z) perceptions of the types of beauty depicted by beauty SMIs? These questions are answered through a content analysis of the top-100 beauty influencers and interviews with 20 Gen Z consumers analyzed using Interpretative Phenomenological Analysis The study found that standard beauty ideals are still valuable when used by SMIs, but the weight of each type is more fluid and SMIs can flow between more than one. SMIs are also helping to grow new or nonstandard beauty ideals, categorized as “other.” The study proves extant knowledge evolves and adapts to this new revolutionary digital format and highlights future possible paths for the future of Gen Z beauty advertising.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it