The Landscape in Global Tobacco Control Research: A Guide to Gaining a Foothold
Bibliographic record
Abstract
Smoking prevalence is shifting from more- to less-developed countries. In higher-income countries, smoking surveillance data, tailored treatments, public health campaigns, and research-based policy implementation have led to a decrease in tobacco use. In low- and middle-income countries, translating research into practice and policy is integral for tobacco control. We describe the landscape of existing resources, both financial and structural, to support global tobacco control research and strengthen research capacity in developing countries. We identify key organizations that support international efforts, provide examples of partnerships between developed and developing countries, and make recommendations for advancing global tobacco research. There is a need for increased commitment from organizations to support global tobacco control research.
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 | 0.085 | 0.082 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.009 | 0.025 |
| Scholarly communication | 0.017 | 0.031 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.013 | 0.023 |
| Insufficient payload (model declined to judge) | 0.019 | 0.015 |
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".