What should the Government do to Stop Epidemic of Smoking among Teenagers in Indonesia?
Bibliographic record
Abstract
Smoking epidemic has occurred among the Indonesian adolescents, as the nearly six out of ten the youth of ages 13 to 15 years smoke daily. In fact, Indonesia has also been known as "the country of smoking baby", as some family smokers have deliberately introduced the way of smoking to their toddlers. In Indonesia, the most new smokers has been ensnared by the tobacco industry, as they started to smoke when they were minors, which are in incapable condition to make rational decisions. In this context, moral question emerge: Is the government obligated to prevent teenagers from taking-up cigarettes, and what should the government do to stop the smoking epidemic among the adolescents in Indonesia? To answer these questions, this paper contain two main study: First, the author has conduct a survey to present an overview about the ensnarement of new smokers and the dilemma of the tobacco retailers in selling cigarettes to minors in Indonesia. Second, the author presents an overview of the negative impacts of tobacco on children and an obligation analysis of the government to prevent adolescents from taking-up cigarettes. It will then propose some approach to stop smoking epidemic among the adolescents in Indonesia.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".