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Record W4403509833 · doi:10.1177/09593535241283729

Fitting comfortably together: Doing and imagining gender and sexuality in personal assistance

2024· article· en· W4403509833 on OpenAlexaff
Harvey Humphrey, Edmund Coleman-Fountain, David Abbott, Alex Toft

Bibliographic record

VenueFeminism & Psychology · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicHealthcare innovation and challenges
Canadian institutionsNorthern Ontario Academic Medicine Association
FundersSchool for Social Care ResearchNational Institute for Health and Care Research
KeywordsHuman sexualityGender studiesPsychologySociologyAestheticsSocial psychologyArt

Abstract

fetched live from OpenAlex

This article reports a UK study in which 12 young disabled adults took part in in-depth qualitative interviews that explored how gender and sexuality mattered for their personal assistance. We draw on queer, trans, and disabled feminist research and theory to discuss the ways that genders and sexualities are part of the decisions that young disabled adults make when arranging and managing their personal support, from drafting support plans and recruitment adverts to working out how to share personal space and display the body. We discuss how gender and sexuality are part of the interactions that young disabled adults imagine as necessary for them and their personal assistants (PAs) to be able to work together. The article also offers a creative approach to representing the data. Composed from young disabled adults’ words following a grounded theory analysis of the data, vignettes were developed to respond to ethical challenges of representing the stories of disabled queer, trans, and nonbinary young people. The article ends by discussing the ethical work of enabling gendered and sexual lives through personal assistance. The project was funded by the National Institute for Health and Care Research (NIHR) School for Social Care.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.734
Threshold uncertainty score0.333

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.093
GPT teacher head0.455
Teacher spread0.362 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations1
Published2024
Admission routes1
Has abstractyes

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