Using ISO 22382 (Tax Stamps) as a Means to Reduce Illicit Trade in Tobacco Products
Bibliographic record
Abstract
I was ISO's Project Leader on ISO 22382, Guidelines for the content, security, issuance and examination of excise tax stamps, the new guidance standards for tax stamps. In this paper I will run through the content of this standard and explain how tax authorities and tax stamp suppliers can adopt its recommendations to counter tobacco product fraud such as smuggling, diversion and counterfeiting. The Standard explains why the use of anti-fraud features on tax stamps requires consideration of the three-way relationship between the authentication feature, the tools required to examine it and the examiner using those tools. In other words, the importance of considering the examiner in the field when specifying tax stamps. It also describes the function of the unique identifier, explaining how this can be applied to the stamp and taxable products to provide the means of tracking and tracing. I will show the practical implications of this in implementing the requirements of the Standard to reduce illicit trade and increase excise tax revenues. In conclusion, I will show how tax stamps designed to meet the guidance in this Standard comply with the requirements of the FCTC Protocol and the EU TPD, thus allowing tax and health regulation authorities to save money by adopting what amounts to a dual-function stamp.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".