Using cohort change ratios to estimate life expectancy in populations with negligible migration: A new approach
Bibliographic record
Abstract
Census survival methods are the oldest and most widely applicable methods of estimating adult mortality, and for populations with negligible migration they can provide excellent results. The reason for this ubiquity is threefold: (1) their data requirements are minimal in that only two successive age distributions are needed; (2) the two successive age distributions are usually easily obtained from census counts; and (3) the method is straightforward in that it requires neither a great deal of judgment nor “data-fitting” techniques to implement. This ubiquity is in contrast to other methods, which require more data, as well as judgment and, often, data fitting. In this short note, the new approach we demonstrate is that life expectancy at birth can be computed by using census survival rates in combination with an identity whereby the radix of a life table is equal to 1 (l0 = 1.00). We point out that our suggested method is less involved than the existing approach. We compare estimates using our approach against other estimates, and find it works reasonably well. As well as some nuances and cautions, we discuss the benefits of using this approach to estimate life expectancy, including the ability to develop estimates of average remaining life at any age. We believe that the technique is worthy of consideration for use in estimating life expectancy in populations that experience negligible migration.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".