Rotation in age patterns of mortality decline: statistical evidence and modeling
Bibliographic record
Abstract
In the context of mortality forecasting, “rotation” refers to the phenomenon that mortality decline accelerates at older ages but decelerates at younger ages. Since rotation is typically subtle, it is difficult to be confirmed and modeled in a statistical, data-driven manner. In this paper, we attempt to overcome this challenge by proposing an alternative modeling approach. The approach encompasses a new model structure, which includes a component that is devoted to measuring rotation. It also features a modeling technique known as ANCOVA, which allows us to statistically detect rotation and extrapolate the phenomenon into the future. Our proposed approach yields plausible mortality forecasts that are similar to those produced by Li et al. [Extending the Lee-Carter method to model the rotation of age patterns of mortality decline for long-term projections. Demography 50 (6), 2037–205, and may be considered more advantageous than the approach of Li et al. in the sense that it is able to generate not only static but also stochastic forecasts.
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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 | 0.029 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".