Bibliographic record
Abstract
"Zappers," or automated sales suppression devices, have brought unheard of efficiencies and economies of scale to a very simple tax fraud - skimming cash sales at point of sale (POS) terminals (electronic cash registers). Until recently the largest tax fraud case in Connecticut, also the "largest computer driven tax-evasion case in the nation," was a zapper case. Stew Leonard's Dairy in Norwalk Connecticut skimmed $17 million in receipts and hid the cash in St. Martin (a Caribbean island). Talal Chahine and his wife, Elfat El Aouar, owners of the La Shish restaurant chain in Detroit Michigan have the dubious honor of replacing Stew Leonard as the leading U.S. zapper fraud case. They zapped $20 million in cash sales and sent the funds to Hezbollah in Lebanon.\nZapper frauds (like electronic cash registers) are not confined to the U.S. Zappers are also a significant problem in Canada, Brazil, Australia, and many countries in the EU. When the U.S. and foreign experiences are considered comparatively, it is not the similarity in the fraud-mechanism (the zapper) that is the most striking - it is the difference in the enforcement mechanism that catches one's attention. In both Canada and Brazil zappers were identified through consumption (not income) tax investigations, and this difference should suggest to U.S. policy-makers that important enforcement opportunities lay within a strengthened State-Federal audit exchange at the retail sales tax level.\nThis paper makes this income tax/retail sales tax connection, and extends it by opening up for consideration the enforcement opportunities that are available through certified tax software solutions under the Streamlined Sales and Use Tax Agreement (SSUTA). An extension of the SSUTA is proposed through the adoption of German Working Group on Cash Register's proposal to use encryption and smart cards in ECRs and POS systems. A certified service provider (CSP) under the SSUTA (as extended) with current levels of technology, could easily be employed not only to assure the States that the correct retail sales tax was being collected and remitted, but also assure the federal government that cash sales were not being skimmed by 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".