Free agents or cogs in the machine? Classed, gendered, and racialized inequities in hazardous working conditions
Bibliographic record
Abstract
INTRODUCTION: Few epidemiologic studies have used relational social class measures based on control over productive assets and others' labor to analyze inequities in health-affecting working conditions. Moreover, these studies have often neglected the gendered and racialized dimensions of class relations, dimensions which are essential to understanding population patterns of health inequities. Our study fills these gaps. METHODS: Using data from the 2002-2018 U.S. General Social Survey, we assigned respondents to the worker, manager, petit bourgeois, or capitalist classes based on their supervisory authority and self-employment status. Next, we estimated class, class-by-gender, and class-by-race inequities in compensation/safety, the labor process, control, and conflict, using Poisson models. We also estimated gender-by-race inequities among workers. RESULTS: We identified substantial class inequities, with worse conditions for workers, which is the largest class within genders and racialized groups, but also disproportionately consists of women and people of color (POC), particularly women of color (WOC). For example, relative to workers, capitalists were less likely to report that safety is not a priority (prevalence ratio [PR]: 0.41, 95% confidence interval [CI]: 0.21, 0.82), repetitive tasks (PR: 0.36, 95% CI: 0.21, 0.61), and lacking freedom (PR: 0.11, 95% CI: 0.05, 0.24). We also identified inequities among workers, with women and POC, particularly WOC, reporting worse conditions than white male workers, especially greater discrimination/harassment (WOC PR: 1.70, 95% CI: 1.36, 2.13). CONCLUSION: We identified substantial inequities in working conditions across intersecting classes, genders, and racialized groups. These inequities threaten workers' health, particularly among women and POC.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".