Bodybuilders as cyborgs: considering the actor-network-theory parallels
Bibliographic record
Abstract
As a registered female athlete with the Ontario Physiques Association (OPA), I have first hand knowledge of bodybuilding at the non-competitive level. I have attended various figure, fitness and bodybuilding competitions and continue to participate in an ongoing dialogue with competing female bodybuilders. I believe that my first hand knowledge and experience allows me, as a member of this community, to provide a unique and in-depth analysis of the culture of female bodybuilding more profoundly than those outside of it. Adopting the role of participant-observer, I will explore the connection between the female bodybuilder as a cyborg and ANT. I will present my study as a micro-ethnography with autoethnography elements framed as a kind of case-study that incorporates both primary and secondary research. In conjunction with relevant academic literature, my analysis will be informed by my ongoing journal and an analysis of popular bodybuilding literature. I hope to understand how my own decision-making process as well as that of other female bodybuilders is subsequently enculturated into a cyborg's mindset. This study does not consider the ethics of building a body to unnatural proportions. I will not debate whether the choices made by a female bodybuilder are right or wrong. All persons shape their bodies in some way, through the food they decide to eat, the cigarettes they smoke, the tattoo or piercing they acquire or the hair colour they select for this season. I will focus only on the ways we may regard a female bodybuilder as a cyborg and show how ANT may help us better understand this phenomenon. It is, however, important to first understand the history and culture of bodybuilding.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".