Growing sense of social status threat and concomitant deaths of despair among whites
Bibliographic record
Abstract
A startling population health phenomenon has been unfolding since the turn of the 21st century. Whites in the United States, who customarily have the most favorable mortality profile of all racial groups, have experienced rising mortality rates, without a commensurate rise in other racial groups. The two leading hypotheses to date are that either contemporaneous economic conditions or longer-term (post-1970s) economic transformations have led to declining economic and social prospects of low-educated whites, culminating in “deaths of despair.” We re-examine these hypotheses and investigate a third hypothesis: mortality increases are attributable to (false) perceptions of whites that they are losing social status. Using administrative and survey data, we examined trends and correlations between race-, age- and, education-specific mortality and a range of economic and social indicators. We also conducted a county-level fixed effects model to determine whether changes in the Republican share of voters during presidential elections, as a marker of growing perceptions of social status threat, was associated with changes in working-age white mortality from 2000 to 2016, adjusting for demographic and economic covariates. Rising white mortality is not restricted to the lowest education bracket and is occurring deeper into the educational distribution. Neither short-term nor long-term economic factors can themselves account for rising white mortality, because parallel trends (and more adverse levels) of these factors were being experienced by blacks, whose mortality rates are not rising. Instead, perceptions – misperceptions – of whites that their social status is being threatened by their declining economic circumstances seems best able to reconcile the observed population health patterns. Rising white mortality in the United States is not explained by traditional social and economic population health indicators, but instead by a perceived decline in relative group status on the part of whites – despite no actual loss in relative group position. • Rising U.S. white mortality is wide-sweeping, spanning all education levels. • Economic conditions of whites are declining in absolute, but not relative terms. • Declining absolute economic conditions of whites do not explain their rising mortality. • Rising white Mortality is better explained by perceived loss of relative status.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".