Creating Complete Mortality Life Tables for CARICOM: The Cases of Trinidad & Tobago and Jamaica
Bibliographic record
Abstract
Complete population mortality tables (CPMTs) provide mortality rates and life expectancies at successive integer ages starting at age 0 and ending at a sufficiently high age (e.g., age 100 years). CPMTs are important because they enable evidence-based analysis and decision-making across various government sectors. CPMTs are also important for actuaries in developing countries where credible mortality data on insured lives may be scarce. Despite CPMTs’ importance, countries in the Caribbean Community (CARICOM), like many other developing countries, do not have CPMTs. Instead, many CARICOM countries produce their own abridged population mortality tables (APMTs), which are less informative than CPMTs because they provide mortality and life expectancy information for grouped age intervals. As Trinidad and Tobago and Jamaica are two of the largest economies in CARICOM, our goal is to use their most recent existing APMTs and convert them to CPMTs. The mortality plots produced by Trinidad and Tobago and Jamaica APMTs show “accident humps” for both male and female mortality rates, which make them suitable for the Heligman–Pollard (H&P) method of expanding APMTs. Although CARICOM countries are diverse in many ways, they are sufficiently similar that mortality tables produced for Trinidad and Tobago and for Jamaica will be more relevant to CARICOM countries than tables based on the mortality experience of developed countries such as the United States, Canada, the United Kingdom, or members of the European Union. Hence we anticipate that these tables will benefit Trinidad and Tobago and Jamaica and will be used by other CARICOM countries with minimum modifications to aid their governments’ planning.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".