Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.021 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".