Zappers and Phantom-Ware at the FTA: Are They Listening Now?
Bibliographic record
Abstract
When the Federation of Tax Administrators (FTA) held a national Compliance and Education Workshop in Louisville, Kentucky (February 25-27, 2001) one of the invited speakers was Kevin Pratt, Manager, Underground Economy, Canadian Customs and Revenue Authority (CCRA). He spoke on Zappers. To the best of anyone's present recollection, this was the first time zappers had been discussed with a large group of state-level US tax compliance professionals. However, most of the information that the CCRA presented to the FTA in 2001 was not its own - it was derivative. Zapper investigations were not an in-house specialty of the CCRA (although they were a matter of considerable concern). Zapper investigations had been the specialty of the Quebec Ministry of Revenue (MRQ), and it was from the MRQ that the CCRA learned about zappers. Until recently, not much has changed in US state-level tax enforcement with respect to Zapper and Phantom-ware investigations. But, the US attitude is just now beginning to change. Zappers were discussed at the October 2007 FTA/MTC Audit and Technology Workshop, at the March 2008 FTA Compliance Workshop, and also at the 2008 Annual Meeting of the FTA. This paper presents the case that zappers, or more generally automated sales suppression devices are a global problem. Zappers are everywhere because they have entered the blood stream of the commercial market-place. They are frequently the element that makes or breaks a deal for a new ECR or POS system. Businesses large and small are regularly offered opportunities to invisibly skim cash receipts with this technology, and many are taking advantage of it. Tax administrations are responding, and the US needs to join in this effort.More specifically, this paper follows the development of a specific type of tax fraud - skimming cash sales. It considers the traditional manual skimming of double tills, and then the three generations of automated sales suppression technology: (1) self-help phantom-ware, (2) factory-installed phantom-ware, and then (3) zappers. The paper then indicates that there are signs in the market-place that we have begun to see a fourth generation of this technology in (4) internationally developed, distributed, and customized zappers.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.009 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.030 | 0.006 |
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".