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Record W4280585368 · doi:10.1111/1467-9566.13481

‘We're welcomed into people's homes every day’ versus ‘we're the people that come and arrest you’: The relational production of masculinities and vulnerabilities among male first responders

2022· article· en· W4280585368 on OpenAlexaff
Skaiste Linceviciute, Damien Ridge, Chantal Gautier, Alex Broom, John L. Oliffe, Coral J. Dando

Bibliographic record

VenueSociology of Health & Illness · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsProduction (economics)SociologyPsychologyGender studiesEconomics

Abstract

fetched live from OpenAlex

Encouraging men to open-up about their feelings is a new cultural directive, yet little is known about how this works in practice, including to promote mental health. Ideals of hegemonic masculinity may be increasingly tolerating expressions of vulnerability in some areas of social life. However, the expression of vulnerability in paid work and/or career situations is regulated by organisational ideals and circumstances that may also produce distress. To address uncertainty in the literature, we investigated the experiences of men in traditionally male dominated professions, namely first responders (police, paramedics, and firefighters/rescue). Twenty-one UK based men of diverse ranks and experience currently working within first responder services participated in semi-structured telephone interviews. Distress was positioned as an inevitable part of the work. Yet, striking differences in institutionalised ways of expressing vulnerabilities differentiated the experiences of frontline workers, contributing to a wide spectrum of men's silence right through to relative openness about vulnerability, both in the workplace and domestic spheres. The findings provide importanat insights into how vulnerability is institutionally regulated, illuminating and contrasting how the possibilities for male vulnerabilities are socially produced.

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)0.003
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 score0.217
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.003
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 head0.050
GPT teacher head0.306
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

Citations9
Published2022
Admission routes1
Has abstractyes

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