Bibliographic record
Abstract
Why is a specific, narrow, and stereotypical standard of beauty typically perceived as more valuable, more worthy, and more desirable than others? Commercial and mainstream media play a significant role in carefully crafting societal expectations of beauty standards and ideals. This research project analyzes well-known body-positive advertisements, campaigns, and popular mainstream media to identify trends and embedded messages that reinforce negative and stereotypical beauty ideals. This research takes a critical perspective on the ways in which commercial media can influence the way we view others and ourselves. This research suggests that unrealistic and narrow beauty ideals have been created and maintained through media with the ultimate goal of making a profit from consumers’ insecurities. The overarching contradictions and communicated messages of the analyzed advertisements and literature are reflected and communicated through text and photography in a digital magazine format entitled “Am I Pretty?”. This project utilizes messages and themes often communicated indirectly by commercial media and flips those messages back to the reader, encouraging deeper critical reflection about media messages of beauty we often passively internalize. “Am I Pretty?” is a critical reflection of beauty ideals embedded in mainstream media that we so blindly value and look up to.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".