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Record W2942476135 · doi:10.1177/2156869319844805

Ordinary Lives and the Sociological Character of Stress: How Work, Family, and Status Contribute to Emotional Inequality

2019· article· en· W2942476135 on OpenAlexafffund
Scott Schieman

Bibliographic record

VenueSociety and Mental Health · 2019
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchCenters for Disease Control and Prevention
KeywordsSociologyMental healthScholarshipSocial psychologyPsychologySociological theoryStressorEpistemologySocial sciencePsychotherapist

Abstract

fetched live from OpenAlex

It has been thirty years since the publication of Leonard Pearlin’s (1989) “The Sociological Study of Stress.” This classic work left an indelible mark, shaping the way the field thinks about stressors, their emotional consequences, and the factors that influence the nature of the links between stressors and outcomes. In this essay, I dialogue with that paper—not with a comprehensive summary of the field but rather with a sharper focus on a few core themes that have inspired the direction and current parameters of my scholarship.Pearlin’s theorizing and empirical work on social roles provides a foundation for the sociological study of stress and mental health. I describe the ways his ideas about role strains have influenced my thinking and development around themes like the Stress of Higher Status model, and I propose new directions for research on topics like distributive justice. Pearlin’s ideas hold a special place in the history of social stress research—and the many intellectual puzzles that he proposed remain and provide fertile terrain for advancing knowledge. A greater integration and synthesis of theory and evidence in the sociology of mental health, sociology of emotion, social psychology, stratification and work, occupations, and organizations will help guide such innovations.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.054
GPT teacher head0.386
Teacher spread0.332 · 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 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

Citations23
Published2019
Admission routes2
Has abstractyes

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