Life Expectancy Reversals in Low‐Mortality Populations
Bibliographic record
Abstract
Abstract Behind the steady march of progress toward longer life expectancy in many low‐mortality countries, there have been setbacks even before the Covid‐19 pandemic. In this paper, we use an exploratory approach to describe the temporal structure, age patterns, and geographic aspects of life expectancy reversals. We find that drops in life expectancy are often followed by larger than average improvements, which tells us that most reversals are transitory with little long‐term influence. The age structure of mortality decline when life expectancy falls is tilted toward older ages, a pattern that is quite different from the general pattern of mortality improvement. Geographic analysis shows that mortality reversals are correlated across neighboring countries like Italy and France, or Canada and the United States. These findings are consistent with contagious disease and weather being important causes of life expectancy reversals. We conclude with a discussion of implications for formal modeling and forecasting of mortality to accommodate these patterns that violate some standard assumptions.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".