Bibliographic record
Abstract
A popular YouTube video shows a 2-year-old Indonesian boy, supposedly with a two-pack-a-day habit, deftly smoking a cigarette. The ghastly video illustrates what can happen as tobacco companies push their death wares in the Third World. “While there's been a good bit of success in the U.S. and some of the more developed countries, it's frightening when you see the growth of tobacco use in the developing world,” said Robert T. Croyle, Ph.D., director of the Division of Cancer Control and Population Sciences at the National Cancer Institute. According to the Centers for Disease Control and Prevention, tobacco kills more than 5 million people per year worldwide—more than HIV, tuberculosis, and malaria combined. The CDC estimates that more than 1 billion people will die this century from tobacco-related illness unless urgent action is taken. The World Health Organization is well aware of these statistics. To date, 172 countries have adopted the WHO's Framework Convention on Tobacco Control (FCTC), the world's first public health treaty. The framework, which uses a global approach to combating the tobacco epidemic, requires tobacco products to carry warning labels covering at least 30%–50% of the package's principal display areas—preferably with graphic pictures or illustrations—portraying the harmful effects of tobacco use. Although the U.S. has not officially signed on to WHO FCTC, the new U.S. warning labels match the labeling initiatives efforts adopted by countries that have.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.221 | 0.102 |
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".