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Record W2992083107

Deaf Cultural Identification, Cochlear Implants, and Life Satisfaction

2019· article· en· W2992083107 on OpenAlexaffvenue
Kristen Elizebeth Mulderrig, Sean M. Rogers

Bibliographic record

VenueCanadian acoustics · 2019
Typearticle
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsMacEwan University
Fundersnot available
KeywordsAcculturationLife satisfactionDeaf communityPsychologyIdentification (biology)Scale (ratio)Deaf cultureHearing lossCultural identitySign languageAudiologySocial psychologyMedicineEthnic groupSociologyLinguisticsGeographyFeeling
DOInot available

Abstract

fetched live from OpenAlex

Cultural identification within the Deaf community is a new field of research that looks at the differences in acculturation between deaf individuals. Glickman (1993) created a Deaf Identity Development Theory, which outlines that deaf individuals either identify with the hearing community, the deaf community (immersion), both communities (bicultural), or do not necessarily identify with either (marginal). Research has not looked directly at the effects cochlear implants (CI’s) have on the overall life satisfaction and well-being of these individuals and how the implants may create changes to their cultural identification. This study examined the link between cochlear implants, Deaf cultural identification and overall life satisfaction within the Deaf community and hypothesises that individuals with cochlear implants and strong culture identification will show significantly higher levels of overall life satisfaction than those who do not. A sample of deaf individuals ages 18 and older were given three measures: the Deaf Identity Development Scale (DIDS), the Deaf Acculturation Scale (DAS) and the Satisfaction with Life Scale (SWLS). The results found a significant correlation between life satisfaction and cultural identification, but no significant correlation between CI’s and life satisfaction or cultural identification.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.204
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.021
GPT teacher head0.289
Teacher spread0.268 · 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.

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

Citations6
Published2019
Admission routes2
Has abstractyes

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