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Record W1592344417 · doi:10.18061/dsq.v35i1.4312

Listen and Speak: Power-Knowledge-Truth and Cochlear Implants in Toronto

2015· article· en· W1592344417 on OpenAlexaffabout
Tracey Edelist

Bibliographic record

VenueDisability Studies Quarterly · 2015
Typearticle
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicalizationSign languageCochlear implantPower (physics)SociologySign (mathematics)GovernmentalityReverenceHegemonyPsychologyMedicineLinguisticsAudiologyPolitical scienceLawPsychiatry

Abstract

fetched live from OpenAlex

Cochlear implants and auditory-verbal therapy are the latest techniques and technologies used to make deaf people learn to listen and speak. This paper provides a genealogical analysis of the Cochlear Implant Program at SickKids Hospital in Toronto, Ontario, Canada and shows how this program exemplifies the medicalization of deafness while denying deaf children the opportunity to learn sign language. Using Foucault's concept of governmentality, the relations between power, knowledge, truth and their influences on the program's practices are revealed in order to provide insight into Canadian society's conceptions of deafness. This analysis reveals the Cochlear Implant Program as a capitalist establishment that is supported by unquestioned reverence of modern medicine and technology, oriented by a quest for normalcy. The paper concludes by encouraging members of the Deaf community and their supporters to challenge the hegemony of normalcy by utilizing alternate research-based knowledge-truths of cochlear implants and sign language.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score0.745

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.087
GPT teacher head0.425
Teacher spread0.337 · 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 designObservational
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

Citations5
Published2015
Admission routes2
Has abstractyes

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