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Record W2922284214 · doi:10.1080/09687599.2019.1584093

Disabled men with muscular dystrophy negotiate gender

2019· article· en· W2922284214 on OpenAlexaff
David Abbott, John Carpenter, Barbara E. Gibson, Jon Hastie, Marcus Jepson, Brett Smith

Bibliographic record

VenueDisability & Society · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Rights and Representation
Canadian institutionsUniversity of Toronto
FundersSchool for Social Care ResearchNational Institute for Health and Care Research
KeywordsDisability studiesNegotiationPsychologyDuchenne muscular dystrophyGender studiesEmpirical researchDevelopmental psychologyGerontologySociologyMedicineSocial science

Abstract

fetched live from OpenAlex

Disability is often portrayed as a one-dimensional category devoid of further intersections. Work which has addressed the intersection of disability and male gender has rarely considered different types of disability or impairment, or foregrounded the experiences of disabled men themselves. This article is based on empirical work carried out in England with men who have Duchenne muscular dystrophy (DMD). We explored with participants their sense of themselves as men and their commonalities and differences with other men. Findings suggest that men with DMD claim, reject and redefine what it meant to them to be men. Doing gender was often heavily reliant on the availability and permission of others. Our study highlights the usefulness of exploring gender with men with particular experiences of disability and of looking at how this might change over a life course, especially when the nature and extent of the life course is a precarious one.

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)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0130.020
Scholarly communication0.0070.004
Open science0.0010.012
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.017
GPT teacher head0.289
Teacher spread0.272 · 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 designQualitative
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

Citations30
Published2019
Admission routes1
Has abstractyes

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