Bibliographic record
Abstract
Sales transaction taxes are highly susceptible to technology fraud, which is an inevitable result of today’s widespread reliance on technology to document taxed transactions. Technology can be (and is) manipulated to defeat the collection of these taxes. Both the U.S. retail sales tax (RST) and the European value added tax (VAT) are vulnerable to technology-based fraud. This Article concerns sales suppression — intentionally not recording sales — in the RST, and at the final stage of the VAT, the retail stage, when tax is collected from final consumers. The modern electronic cash register (ECR)/point of sale (POS) system is vulnerable to fraud. These devices are essentially computers with programming that is molded to meet the commercial needs of any particular business. This Article will focus on a particular POS system called Profitek, manufactured in Vancouver by InfoSpec, which uses an MS SQL server, and can be purchased with a dedicated sales suppression device — the Profitek Zapper. The cash register/POS market divides along database lines and the market further subdivides when attributes such as operator language preferences are considered. The market for POS systems is both niche and international, and so are sales suppression software applications. It is common, therefore, to find that the same person who sells an ECR/POS system is also able to provide the business with the zapper that can suppress sales recorded in that specific system. An application that effectively manipulates the digital records of a specific POS system will quickly travel to other countries and states with the associated POS system for which it was designed. This Article follows the InfoSpec/Profitek system and its associated zapper as it migrated from the Canadian restaurant market into the U.S. market.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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; both teacher heads agree on what is shown here.
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".