Understanding the motivations of contraband tobacco smokers
Bibliographic record
Abstract
Aim: This study explored the motivations behind illicit tobacco use in Australia. A key focus was to investigate the hypothesis that the primary motivation for illicit tobacco use is its low cost in comparison to the price of legal tobacco.Methods: An Australian tobacco usage telephone survey was conducted in 2007. Illicit tobacco smokers completed a longer version of the questionnaire, with questions relating to illicit tobacco usage and perceptions.Findings: Of the current smokers of illicit tobacco surveyed, almost half would consider increasing illicit tobacco consumption if the cost of legal tobacco were to increase to four times the price of illicit tobacco, although others stated consumption would remain the same regardless of such a price change. Almost all former smokers of illicit tobacco claimed that price did not influence the decision to stop smoking illicit tobacco.Conclusions: Some illicit tobacco smokers appear to be sensitive to the price of tobacco products, but price is not always an underlying motivator. Personal preference and supply also influence illicit tobacco consumption. The findings suggest that reducing the availability of illicit tobacco would be a useful strategy for combating the growing illicit tobacco problem.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".