Exoskeletons, Transhumanism, and Culture: Performing Superhuman Feats
Bibliographic record
Abstract
In this article we study how exoskeletons are construed through three dominant but interrelated presentations: 1) popular science journalism, 2) advertisements of military innovation, and 3) blockbuster science fiction film. We address how these media function to alter attitudes about the future. The attitudinal framing of the exoskeleton asks that people aspire to transhumanism, and that they “imagine the possibilities in the near future of dramatically enhance[ed) human mental and physical capacities” [4). In this case, they are being encouraged to visualize a militarized future. More so, the rhetorical proposition calls upon people to identify personally with the technology. The rhetoric conveys a militarized identity through celebration, anticipation, and to an extent, panic. It unites excitement and fear through entertainment, but it also succeeds in distorting the conversation of what exoskeleton technology would actually mean in a nonfictional, socio-political, or military context. Michael, Fusco, and Michael [5) identify the “socio-ethics” concerning emerging inventions, as “the moral principles which govern a particular society at large.” We seek in our work to disclose some of the socio-ethical cultural manipulation conveyed through popular channels concerning exoskeletons.
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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 | 0.003 | 0.003 |
| 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 | 0.021 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".