Divergence in age-patterns of mortality change drives international\n divergence in lifespan inequality
Bibliographic record
Abstract
In the past six decades, lifespan inequality has varied greatly within and\namong countries even while life expectancy has continued to increase. How and\nwhy does mortality change generate this diversity? We derive a precise link\nbetween changes in age-specific mortality and lifespan inequality, measured as\nthe variance of age at death. Key to this relationship is a young-old threshold\nage, below and above which mortality decline respectively decreases and\nincreases lifespan inequality. First, we show that shifts in the threshold's\nlocation modified the correlation between changes in life expectancy and\nlifespan inequality over the last two centuries. Second, we analyze the post\nSecond World War trajectories of lifespan inequality in a set of developed\ncountries, Japan, Canada and the United States (US), where thresholds centered\non retirement age. Our method reveals how divergence in the age-pattern of\nmortality change drives international divergence in lifespan inequality. Most\nstrikingly, early in the 1980s, mortality increases in young US males led\nlifespan inequality to remain high in the US, while in Canada the decline of\ninequality continued. In general, our wider international comparisons show that\nmortality change varied most at young working ages after the Second World War,\nparticularly for males. We conclude that if mortality continues to stagnate at\nyoung ages, yet declines steadily at old ages, increases in lifespan inequality\nwill become a common feature of future demographic change.\n
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".