Habilitation beyond the Bionic Metaphor: Producing Deafnesses of the Future
Bibliographic record
Abstract
In this article, we travel back to the early days of experimental use of cochlear implants (CIs) in the 1970s, when unsettled expectations of the device and broad investigations of its effects began to settle and center on speech outcomes. We describe how this attention to speech outcomes coalesced into specific understandings of what CIs do, and how implicit or explicit understandings of CIs as bionic devices that normalize hearing influenced research on and expectations of CIs into the present. We conclude that accumulated evidence about what is known and unknown about experiences and materialities with CIs calls for a decisive break from the metaphor of the bionic ear. This shift would create a space to reconsider the "deafness of history and the present," as well as experiences of brain-computer interfaces that are inclusive of nonnormative life. This article is based on fieldwork in research and clinical facilities in Australia, Canada, and the United States. It included forty-three interviews with clinical experts and leading researchers in the fields of audiology, psychoacoustics, and neuroscience, among them scientists involved in the development and commercialization of one of the first CIs.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".