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Record W3205706713 · doi:10.1080/17458927.2021.1874194

No hearing without signals: imagining and reimagining transductions through the history of the cochlear implant

2021· article· en· W3205706713 on OpenAlexaff
Stephanie Lloyd, Alexandre Tremblay

Bibliographic record

VenueThe Senses and Society · 2021
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsAudiogramConceptualizationCochlear implantAudiologyVariety (cybernetics)PsychologySet (abstract data type)Perspective (graphical)Hearing lossCognitive psychologyArtLinguisticsMedicineComputer scienceVisual arts

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.738
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.047
GPT teacher head0.279
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2021
Admission routes1
Has abstractyes

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