Bibliographic record
Abstract
In the fall of 2018 WGSN (World Global Style Network) ran a report on the emerging “alien beauty” trend, which they defined as “an otherworldly aesthetic inspired by extraterrestrial life forms … signifying a new rebellious attitude towards quintessential beauty norms” (Bailey). Instagram is one of the largest platforms to represent the trend of alien beauty, presented by a thriving community of makeup artists pushing the boundaries of conventional beauty practices. These artists are developing otherworldly and exaggerated makeup looks created through the combination of makeup, fashion, technology, and social media. The following research attempts to outline elements of beauty that are engaged with through alien beauty, and through creative practice presents them on conventionally beautiful bodies to demonstrate new, challenging version of beauty. Alien beauty selfies shared via Instagram can be re-contextualized to challenge existing examples of art, nature, and beauty. Through practice-based methodology and theories of posthumanism, this piece explores the changing ideals of beauty manifested with the support of technology and social media as well as how the term “alien beauty” manifests as a current trend. Considering the re-imagined paintings created to explore alien beauty, they reveal how beauty has been traditionally constructed through a colonial, heteronormative, hegemonic gaze and how “alien” is therefore a form of escapism and rebellion.
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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.069 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".