Analysis of North American Newspaper Coverage of Bionics Using the Disability Studies Framework
Bibliographic record
Abstract
Bionics are a set of technology products that are constantly evolving. Bionics are proposed as body add-ons or replacement for many body parts (ears, eyes, knees, neural prostheses, joints, muscles, kidney, liver, cartilage lungs, discs, pancreas, dental pulp, skin, hippocampus, legs and hands), and functions such as speech. Two main applications of bionic products are discussed; one being for the restoration of body abilities to a species-typical norm and the other being the addition of abilities to the body that are not species-typical. Disabled people are one main group perceived to be in need of therapeutic interventions that use various bionic products. So far, therapeutic interventions are about restoration to the species-typical norm. However, therapeutic bionic products increasingly give the wearer beyond normal body abilities (therapeutic enhancements). Many so-called non-disabled people want the same enhanced body-abilities especially through non-invasive bionic products (e.g., non-invasive brain machine interfaces, exoskeletons). The media has the ability to shape public perceptions with numerous consequences. The purpose of this study was to provide quantitative and qualitative data on how bionic technologies and its users are portrayed in North American newspapers. Data was obtained from 1977 to 2013 from the Canadian Newsstand complete database which covers over 300 English language Canadian newspapers and two Canadian newspapers, one with national focus (The Globe and Mail) and one with local focus (Calgary Herald), and from 1980–2013 from one American newspaper with national reach (The New York Times). The study found (a) an almost always positive portrayal of bionics; (b) coverage of bionics mostly within a medical framework; (c) a predominantly stereotypical and negative portrayal of individuals with disabilities; and (d) a hierarchy of worthiness between different assistive devices such as a reporting bias favoring artificial legs over wheelchairs. At the same time the study did not find any engagement with social and ethical issues that are already raised about bionics in the literature, such as the increasing desire for enhancements, the use of bionics for non-therapeutic purposes and the issues socially disadvantaged people might face in the wake of bionic advancements. We posit that the newspapers generate a bionic discourse culture that is problematic for disabled people and other socially disadvantaged groups and that they do not prepare readers for the challenges that bionic advancements will pose for the general population in the future.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.019 | 0.042 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".