Zappers, Phantomware and Other Sales Suppression Software in the State of Washington
Bibliographic record
Abstract
Electronic sales suppression (ESS) is a fraud that has been a (prominent) feature of the North American retail business since at least 1996. The first EES case in the US dates from 1981. ESS is a global problem. Depending on the jurisdiction, and the research study consulted, ESS is estimated to be present in 34% (of Canadian), 50% (of German – two studies), and 70% (of Swedish and Slovenian) businesses. It may be the case today, that “you cannot leave home without” encountering (or participating in) ESS.\nThe most common types of sales suppression technology are Zappers and Phantomware programming. In some instances, sales suppression is a personal (hands-on) service offered by installers or ECR/POS sales representatives. This is Sales Suppression as a Service or SSaaS. Recently suppression has entered the Dark Cloud, a fully automated manipulation of sales data that (physically) takes place off shore and uses internet-based data transfers.\nThe common solution in all cases is digital security, or fighting technology with technology. In the US, ESS has funded common criminals, organized crime syndicates, foreign and domestic terrorist organizations. US suppression cases have involved celebrity chefs, sitting members of Congress, the funding arm of Hezbollah, popular grocery store chains, restaurants, bars/ strip clubs, and small owner-operated pizza parlors. The technological response in the US has been weak. For some reason, the US has been very slow in taking up the technology-with-technology fight.\nGiven that the State of Washington collects 47.3% of its revenue (not including local government taxes) from the retail sales tax, and that technology has been the backbone of the State’s economy for years, it is only natural that Washington would take a US leadership position in this effort. Washington still trails by a wide margin the international efforts. The US has a lot to learn from jurisdictions like Belgium, Brazil, Canada (notably the provinces of Quebec and Ontario), China, Croatia, Italy, Russia, Rwanda, Sweden, and by January 1, 2 each of the members of the Gulf Cooperation Council (the United Arab Emirates, Bahrain, Saudi Arabia, Oman, Qatar and Kuwait).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".