Occupational class, capitalist class advantage and mortality among working-age men
Bibliographic record
Abstract
BACKGROUND: Disparities in mortality have been firmly established across occupational grades and the incomes they earn, but this line of research has failed to include individuals' relationships to capital, as suggested by class analysists. METHODS: According to Wright's classification, the research generated 10 mutually exclusive classes based on occupation and investment income: worker; capitalist worker; professional; capitalist professional; supervisor; capitalist supervisor; manager; capitalist manager; self-employed; and capitalist self-employed. The study participants (n=268 239) were randomly selected from the Statistics Finland population database and represent 33% of Finnish men aged 30-64 years. The mortality data were monitored over the 1995-2014 period. RESULTS: The sociodemographic-adjusted HRs for mortality were lowest for capitalist managers (HR 0.50; 95% CI 0.36 to 0.69) as compared with that for workers without a capitalist class advantage. A positive occupational class gradient was found from managers to supervisors to workers. The capitalist class advantage independently affected the disparities in mortality within this occupational hierarchy. CONCLUSION: Different occupational class locations protect against premature death differently, and the capitalist class advantage widens the premature-death disparities among the occupational classes. To monitor and explain social inequalities in health in a more nuanced way, future research on investment income as well as the operationalisation of the capitalist class advantage is encouraged.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".