Bibliographic record
Abstract
The ITC Project would like to acknowledge and thank the principal investigators, researchers, staff and research assistants of the 2016 (wave 1) ITC Four Country Smoking and Vaping Survey spanning across the United States, Canada, England and Australia, who have contributed their efforts in developing the survey questions, preparing the protocols for data collection and conducting the data analysis for all papers in this supplement. Our thanks also go to the survey firms in these four countries for collecting the data. The ITC Project also wishes to acknowledge the main funding agencies that contributed to the ITC Four Country Smoking and Vaping Survey: the US National Cancer Institute (P01 CA200512), the Canadian Institutes of Health Research (FDN-148477) and the National Health and Medical Research Council of Australia (APP 1106451). Additional funding support was provided by the Ontario Institute for Cancer Research. Many thanks to Anne C. K. Quah, the ITC Managing Director and Senior Research Scientist, who led the coordination and every aspect of the organization of this excellent compilation of articles in her usual tireless way, assisted by Janine Ouimet, the project manager of the 2016 ITC Four Country Smoking and Vaping Survey. We thank K. Michael Cummings and Geoffrey T. Fong for their leadership in this project. We are grateful to the Senior Editor at Addiction for his insightful feedback on the articles in this supplement and to Molly Jarvis for coordinating the articles submission. Thanks also go out to Lalaine Bacea, the production editor, and Silvana Losito at Wiley for their excellent assistance in the production of the articles. The International Tobacco Control Policy Evaluation Project (the ITC Project) is an international research collaboration of more than 150 tobacco control researchers and experts from 29 ITC countries (Canada, United States, United Kingdom, Australia, Ireland, Thailand, Malaysia, China, Japan, Spain, Greece, Hungary, Poland, Romania, Republic of Korea, New Zealand, Mexico, Uruguay, France, Germany, the Netherlands, Brazil, Mauritius, Bangladesh, Bhutan, India, Kenya, Zambia and United Arab Emirates–Abu Dhabi) who have come together to conduct research to evaluate the impact of tobacco control policies of the WHO Framework Convention on Tobacco Control (FCTC), the world's first health treaty. These policies include more prominent warning labels (including graphic images), comprehensive smoke-free laws, restrictions or bans on tobacco advertising, promotion and sponsorship, higher taxes on tobacco products, removal of potentially deceptive labeling (e.g. ‘light’ and ‘mild’ and packaging design that lead consumers to the misperception that certain brands may be less harmful), promotion of cessation, education of the public on the harms of tobacco, reduction of illicit trade, reduction of youth access and product regulation. The ITC team in each country conducts longitudinal cohort surveys and capitalizes on natural experiments to evaluate the impact of these policies over time. ITC Surveys contain more than 150 measures of tobacco policy impact and have been conducted in countries inhabited by more than 50% of the world's population, 60% of the world's smokers and 70% of the world's tobacco users. The ITC Project has recently expanded its scope to examine the impact of policies and regulations on alternative nicotine delivery products, which is the focus of this Supplement.
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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.297 | 0.127 |
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".