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Record W2076481759 · doi:10.1075/ni.17.2.06hol

Narratives of identity

2007· article· en· W2076481759 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNarrative Inquiry · 2007
Typearticle
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNarrativeIdentity (music)Construct (python library)SociologyGender studiesPsychologyLinguisticsAestheticsArtPhilosophy

Abstract

fetched live from OpenAlex

Living in the world as a Deaf person provides a different situatedness in which deaf individuals construct their identity. How does living in the world, different from the hearing majority, influence the ways deaf individuals go about the creative act of constructing identities? Traditionally, researchers of D/deafness have constructed identity categories in order to research identity and hearing loss. For example, there is a distinction made in the literature between deafness (written with a lower case ‘d’) — an audiological state related to having a hearing loss — and Deafness (written with an upper case ‘D’) — a marker of a culturally Deaf identity. This article is about how three women constructed narrative identities relating to hearing loss in life stories. And how they incorporated, resisted, and/or rejected various cultural discourses in narratives they told? Using a poststructural narrative analysis, I explore how identities relating to hearing status were shaped and limited by four discourses at work in the participants’ narrative tellings (discourses of normalcy, discourses of difference, discourses of passing, and Deaf cultural discourses). For example, I discuss how discourses of normalcy and discourses of difference led to the construction of identities based on opposites, in a binary relationship where one side of the binary was privileged and the opposite was “othered”, e.g., hearing/deaf, and Deaf/deaf. Finally, drawing on the work of Judith Butler, I conclude the article with a discussion of some theoretical implications that emerged from using a poststructural narrative analysis.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.622
Threshold uncertainty score1.000

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)0.0010.000

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 head0.095
GPT teacher head0.441
Teacher spread0.346 · 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