Bodybuilding as a Gender Norm Defier: Shredding the Binary Materiality while Rewriting Bodies through Bodybuilding
Bibliographic record
Abstract
This paper presents a peculiar approach of escaping the dual gender-identity conceptualization, using bodybuilding—which is both a sports and corporeal practice—as a tool to shatter gender stereotypes. The bodies that are trans-formed as a result of this practice will be analysed and interpreted as a way to develop a relevant distinction between those bodies that are objectified on stage, due to the sport’s regulations, and those bodies that are located within an artistic and activist frame as “critical flesh.” This analysis looks at the bodies of three artists involved in the practice of bodybuilding to develop their artworks in order to discuss whether or not this corporeal identity built through muscle de-velopment creates a split in the gender discourse and in the expectations that this discourse generates. This approach is developed through the visual and conceptual support from the following artists: Cassils [they/them] (Canada-USA), Francesca Steele [they/them] (UK), and the author of this paper [she/her].
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.050 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".