Bibliographic record
Abstract
In this article, I discuss my “anti-aging” body modification practices as body art. The art documents my bodybuilding programs, self-administered neurotoxin (Botox) injections, and skin resurfacing treatments. Susan Pickard (2020) argues that femininity and aging are associated with the abject. She maps the abject and non-abject onto Simone de Beauvoir’s distinction between immanence and transcendence. Because “abjection should always be understood as an element of [...] oppression” (Pickard 2020, 159), my art practice could be read as an anti-feminist, ageist attempt to expel the abject. After offering a counter-argument that positions my practice as feminist, I use Kathy Acker’s (1993) writing on bodybuilding to offer a third reading. Muscles grow when they are worked until failure. This practice of constantly coming up against the body’s limits is a rehearsal for the ultimate failure of the body: death (Acker 1993). If thanatology is the study of death and dying, bodybuilding is autothanatology. My “anti-aging” interventions are similar; they are inevitable failures that cannot stop the aging process. In this way, my practice is a reminder that the body exists in a state of immanence, even while I may attempt to frame my immanence along transcendental terms.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".