Body Like a Rocket: Performing Technologies of Naturalization
Bibliographic record
Abstract
This article examines how athletes embody and perform technologies in ways that question the human form. Through a historical review of the political implications of science and technology in the modern Olympics and a close analysis of Speedo bodysuit swimwear featured since the 2000 Summer Olympics, I explore how technologies produce boundaries between bodies fit for competition and deviant bodies along lines of power. The interaction of material technologies and athletic bodies allow both the athlete and viewing community to participate in myths of human progress that is both separate from and reliant on technological enhancement. The sporting event becomes simultaneously a performance of the natural abilities of the human body and the physical enhancement of human ability through a high-tech product available for purchase to anyone with enough capital. Building on the work of feminist and science studies scholars, a close analysis of the material-discursive production of technologies reveals the tangled political investments of powerful groups, nations, and corporations in perpetuating modern narratives of progress. These networks mobilize technologies to define what constitutes human bodies, creating power differentials across subject positions of gender, class, race, nationality, and ability.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".