Body Modification as Body Art: Aging, Abjection, and Autothanatology
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.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".