No hearing without signals: imagining and reimagining transductions through the history of the cochlear implant
Bibliographic record
Abstract
In this article, we explore a set of conceptual and technoscientific shifts that led to reconsiderations of the experience of hearing over the twentieth and twenty-first centuries, and most specifically, hearing through the use of cochlear implants (CIs). In doing so, we focus on the factors that are thought to contribute to CI users’ experiences of sound, including their potentially distinctive sensoria and neural profiles, as they navigate the spaces of their day-to-day lives as both the bearers of objective audiograms and subjective listeners. These factors are increasingly broad, ranging from age of implantation, electroacoustic stimulation sent from the device, and cognitive profiles considered to correlate with complex developmental processes related to early sound environments and language exposure (oral or manual). Hearing, in this perspective, is a phenomenon that varies between individuals, over the course of the life (or day) of a single person, and according to experiences with auditory devices. Such a conceptualization undermines dichotomous representations of hearing and deafness and an increasingly substantial gray zone emerges between the two. Both are ever more conceived of as developmental processes in which a variety of signals and their transductions are considered central to understandings of how experiences of hearing take shape.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.078 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".