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Record W4280503502 · doi:10.1177/09500170221080388

Gender in the Flesh: Allostatic Load as the Embodiment of Stressful, Gendered Work in Canadian Police Communicators

2022· article· en· W4280503502 on OpenAlexafffundabout
Arija Birze, Elise Paradis, Cheryl Regehr, Vicki R. LeBlanc, Gillian Einstein

Bibliographic record

VenueWork Employment and Society · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEmotional Labor in Professions
Canadian institutionsUniversity of OttawaWomen's College HospitalUniversity of Toronto
FundersUniversity of Ottawa
KeywordsAllostatic loadEmbodied cognitionConformityStressorPsychologySocial psychologyAllostasisNorm (philosophy)Clinical psychologyPolitical science

Abstract

fetched live from OpenAlex

Gender and work are important social determinants of health, yet studies of health inequities related to the gendered and emotional intricacies of work are rare. Occupations high in emotional labour – a known job stressor – are associated with ill-health and typically dominated by women. Little is known about the mechanisms linking health with these emotional components of work. Using physiological and questionnaire data from Canadian police communicators, we adopt an embodied approach to understanding the relationship between gender norm conformity, emotional labour, and physiological dysregulation, or allostatic load. For high conformers, emotional labour leaves gendered traces in the flesh via increased allostatic load, suggesting that in this way, gendered structures in the workplace become embodied, influencing health through conformity to gender and emotion norms. Findings also reveal that dichotomous conceptions of gender may mask the impact of gendered structures, obscuring the consequences of gender for work-related stress.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.164

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0090.006
Scholarly communication0.0050.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.046
GPT teacher head0.330
Teacher spread0.285 · 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 designObservational
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

Citations5
Published2022
Admission routes3
Has abstractyes

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