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Record W2020017216 · doi:10.1097/aud.0b013e318031547a

Music Recognition, Music Listening, and Word Recognition by Deaf Children with Cochlear Implants

2007· article· en· W2020017216 on OpenAlexfundno aff
Chisato Mitani, Takayuki Nakata, Sandra E. Trehub, Yukihiko Kanda, Hidetaka Kumagami, Kenji Takasaki, Ikue Miyamoto, Haruo Takahashi

Bibliographic record

VenueEar and Hearing · 2007
Typearticle
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsActive listeningCochlear implantPsychologyAudiologyMusicalWord recognitionSyllableSet (abstract data type)MedicineSpeech recognitionLinguisticsCommunicationArtVisual artsComputer scienceReading (process)

Abstract

fetched live from OpenAlex

OBJECTIVES: To examine the ability of congenitally deaf children to recognize music from incidental exposure and the relations among age at implantation, music listening, and word recognition. DESIGN: Seventeen child implant users who were 4 to 8 yr of age were tested on their recognition and liking of musical excerpts from their favorite television programs. They were also assessed on open-set recognition of three-syllable words. Their parents completed a questionnaire about the children's musical activities. RESULTS: Children identified the musical excerpts at better than chance levels, but only when they heard the original vocal/instrumental versions. Children's initiation of music listening at home was associated with younger ages at implantation and higher word recognition scores. CONCLUSIONS: Child implant users enjoy music more than adult implant users. Moreover, younger age at implantation increases children's engagement with music, which may enhance their progress in other auditory domains.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.051
GPT teacher head0.292
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), 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

Citations72
Published2007
Admission routes1
Has abstractyes

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