Ordinary Lives and the Sociological Character of Stress: How Work, Family, and Status Contribute to Emotional Inequality
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".