‘It feels like being Deaf is normal’: an exploration into the complexities of defining D/deafness and young D/deaf people's identities
Bibliographic record
Abstract
In this article we examine the ways in which young D/deaf British people express and experience their identities and how their D/deafness intersects with other self‐identifications. We examine the controversial debates within D/deaf communities, cultures and studies about D/deafness as disability versus D/deafness as linguistic minority. We explore the ways in which ‘Deaf ’ and ‘deaf ’ definitions and identities contradict, overlap, coexist and compete. At the same time we discuss the problems with binary constructions of deaf/hearing or Deaf/deaf for capturing the full experiences of young D/deaf people's lives. We consider the reasons why there is such a dearth of research within the social sciences which focuses on young D/deaf people's lives and discuss the complexities of conducting this type of research. Young D/deaf people's articulations of identities and cultural experiences are presented. We conclude with suggestions for researchers and also with a hope that the current D/deaf challenges towards the hearing world and deaf challenges within the Deaf world may bring future possibilities and opportunities for D/deaf young people in the U.K.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.025 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".