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Record W4405110263 · doi:10.1080/14649365.2024.2435874

‘You’ve got to learn how to be more vulnerable’: exploring the shifting geographies of men living with chronic illness

2024· article· en· W4405110263 on OpenAlexafffund
Robert Wilton, Ann Fudge Schormans

Bibliographic record

VenueSocial & Cultural Geography · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsMcMaster University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSociologyGender studiesGerontologyAestheticsMedicineArt

Abstract

fetched live from OpenAlex

In this paper, we contribute to geographic scholarship on masculinities through an examination of the interweaving of gender and disability in the shifting social geographies of men who live with chronic physical illness. Using an in-depth qualitative and visual arts methodology with men who live with MS and Fibromyalgia, we consider how their changing physical and cognitive capacities can make it difficult to approximate an able-bodied masculinity. Our analysis highlights two key themes. First, the diverse physical and cognitive impacts of chronic illness pose problems for masculine embodiment in different ways in different places. Problems are perhaps most acute in the context of the workplace but also impact gendered domestic geographies. Second, gendered relations of care and support are transformed by experiences of chronic illness. On the one hand, men are increasingly dependent on others for support. This places new demands on existing relationships that impact the doing of gender. On the other hand, changes wrought by chronic illness involve some men more actively in the provision of care. We examine what this means for the re-gendering of 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0040.000
Scholarly communication0.0010.001
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 head0.038
GPT teacher head0.295
Teacher spread0.256 · 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.

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

Citations2
Published2024
Admission routes2
Has abstractyes

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