Bibliographic record
Abstract
More than a quarter of the world's \n population is between the ages of 10 and 24. Most (86 \n percent) of the world's 1.7 billion young people live \n in developing countries, where they are often 30 percent or \n more of the population. At first glance, youth appears to be \n a relatively healthy although not hazard-free period of \n life. Young people account for 15 percent of the disease and \n injury burden worldwide and over one million die each year, \n mainly from preventable causes. Nonetheless, roughly 70 \n percent of premature deaths among adults can be linked to \n behavior initiated during adolescence, such as tobacco use, \n poor eating habits, and risky sex. Investing in health and \n development of young people is not only the right thing to \n do, it's the smart thing for countries that want their \n economies to grow faster: 1) reducing HIV infection in young \n people will reduce the devastating economic impact of \n HIV/AIDS; 2) when young people postpone marriage and \n childbearing, family size falls and population growth slows. \n Combined with investments in health and education, these \n changes contribute to higher economic growth and incomes; \n and 3) investments to head off negative behaviors such as \n tobacco use and drug abuse will pay off later for \n individuals and for society.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.282 | 0.086 |
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".