Hiding in plain sight: The rhetoric of bionic contact lenses in mainstream discourses
Bibliographic record
Abstract
This article explores and critiques mainstream speculative news surrounding personal technologies. We focus on news concerning bionic contact lenses, a hardware invention prototype by Google Inc promoted as a ‘future’ personal computing device. Technology is increasingly normalized and configured as inevitable through representations across consumer media outlets. In our analysis of a large corpus of online and print news coverage, we identify three rhetorical strategies that justify it as either a medical/assistive device within a discourse of health, or a device for transhuman enhancement within a discourse of transhumanism. Employing Roland Barthes’s critical theory of myth, we argue that the first medical justification obfuscates but ultimately promotes the second justification, transhuman enhancement. This transhumanist vision endorses enhancement and augmentation without an identifiable purpose or disclosure concerning how people as users might be affected in the future. New media are subtly promoted during invention; yet, their social function, implied ideologies, and commercialized agenda are rarely challenged. We problematize these omissions, and highlight the need for critical dialogue.
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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.012 | 0.055 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| 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".