Measuring the illicit cigarette market in the absence of pack security features: a case study of South Africa
Bibliographic record
Abstract
There are several ways to measure the illicit cigarette market. In South Africa, different methods were used to triangulate results. The aim of this paper is to assist researchers to decide which method is most suitable to their context, especially for countries that do not have security features on cigarette packs (eg, tax stamps). We analysed the methods and results from three published articles that used various approaches to measure cigarette illicit trade in South Africa: (1) gap analysis, (2) price threshold method using secondary data from a national survey, and (3) price threshold method using primary data collected in low socioeconomic areas. We provide methodological insights and background information. We discuss the advantages and disadvantages of each method. The method chosen by researchers will depend on data availability, the existence or absence of security features on cigarette packs and funding. Researchers investigating illicit trade should use more than one method to increase confidence in the obtained results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".