The Serpent of Their Agonies
Bibliographic record
Abstract
BACKGROUND: Social stratification is a well-documented determinant of mental health. Traditional measures of stratification (e.g., socioeconomic status) reduce dynamic social processes to individual attributes downstream of mechanisms that generate stratification. In this study, we measure one process theorized to generate and reproduce social stratification-economic exploitation-and explore its association with mental health. METHODS: Data are from the 1983 to 2017 waves of the Panel Study of Income Dynamics, a nationally representative cohort study (baseline N = 3059). We operationalized "unconcealed exploitation" as the percentage of individuals' labor income they were hypothetically not paid for productive hours. We ascertained psychologic distress and mental illness with the Kessler-6 (K6) scale. RESULTS: We fit inverse probability-weighted marginal structural models and found that for each unit increase in unconcealed exploitation, psychologic distress increased by 1.6 points (95% confidence interval = 0.71, 2.5) on the K6 scale and the odds of mental illness tripled (odds ratio = 3.0, 95% confidence interval = 1.5, 6.1). Results were not driven entirely by overwork and were robust to different inverse probability-weighted estimation strategies and sensitivity analyses. CONCLUSIONS: Exploitation is associated with mental illness. Focusing on exploitation rather than its consequences (e.g., socioeconomic status), shifts attention to a structural process that may be a more appropriate explanatory mechanism, and a more pragmatic intervention target, for mental illness.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".