Understanding the Rapid Increase in Life Expectancy in South Korea
Bibliographic record
Abstract
OBJECTIVES: We assessed life expectancy increases in the past several decades in South Korea by age and specific causes of death. METHODS: We applied Arriaga's decomposition method to life table data (1970-2005) and mortality statistics (1983-2005) to estimate age- and cause-specific contributions to changes in life expectancy. RESULTS: Reductions in infant mortality made the largest age-group contribution to the life expectancy increase. Reductions in cardiovascular diseases (particularly stroke and hypertensive diseases) contributed most to longer life expectancy between 1983 and 2005 (30% in males and 28% in females). Lower rates of stomach cancer, liver disease, tuberculosis, and external-cause mortality accounted for 30% of the male and 20% of the female increase in longevity. However, higher mortality from ischemic heart disease, lung and bronchial cancer, colorectal cancer, breast cancer, diabetes, and suicide offset gains by 10% in both genders. CONCLUSIONS: Rapid increases in life expectancy in South Korea were mostly achieved by reductions in infant mortality and in diseases related to infections and blood pressure.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".