Offspring Socioeconomic Status and Parent Mortality Within a Historical Population
Bibliographic record
Abstract
Considering a network approach to health determinants, we test the hypothesis that benefits of high socioeconomic status (SES) may be transmitted up the generational ladder from offspring to parents. Studies that examine own SES and own health outcomes, or SES of parents and outcomes of young or adolescent children, are common. Those that investigate SES of offspring and their association with parental health are rare. Employing data from a historical population of individuals extracted from a comprehensive population database that links demographic and vital records across generations, this study tests the hypothesis that higher offspring SES associates with lower parental mortality after controlling for parental SES. The sample includes 29,972 individuals born between 1864 and 1883 whose offspring were born between 1886 and 1920. SES is operationalized using Nam-Powers occupational status scores divided into quartiles and a category for farmers. Models assess mortality risk after age 40. Included is a test for whether effects are proportional across parents who died younger and older. Estimated life expectancies across categories of offspring SES conditioned on parental SES are calculated to illustrate specifically how differences in SES relate to differences in years lived. Results indicate a longevity penalty for those whose offspring have low SES and a longevity dividend for those with high-SES offspring. The influence of offspring attributes on well-being of parents points to fluid and myriad linkages between generations.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".