Bibliographic record
Abstract
This article argues for an aesthetic approach to resisting oppression based on judgments of bodily unattractiveness. Philosophical theories have often suggested that appropriate aesthetic judgments should converge on sets of objects consensually found to be beautiful or ugly. The convergence of judgments about human bodies, however, is a significant source of injustice, because people judged to be unattractive pay substantial social and economic penalties in domains such as education, employment and criminal justice. The injustice is compounded by the interaction between standards of attractiveness and gender, race, disability, and gender identity. I argue that we should actively work to reduce our participation in standard aesthetic practices that involve attractiveness judgments. This does not mean refusing engagement with the embodiment of others; ignoring someone’s embodiment is often a way of dehumanizing them. Instead, I advocate a form of practice, aesthetic exploration, that involves seeking out positive experiences of the unique aesthetic affordances of all bodies, regardless of whether they are attractive in the standard sense. I argue that there are good ethical reasons to cultivate aesthetic exploration, and that it is psychologically plausible that doing so would help to alleviate the social injustice attending judgments of attractiveness.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.070 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".