Understanding the Rapid Increase in Life Expectancy in South Korea
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it