Quebec's Sales Recording Module (SRM): Fighting the Zapper, Phantomware, and Tax Fraud with Technology
Bibliographic record
Abstract
On January 28, 2008, Quebec's minister of revenue, Jean-Marc Fournier, announced that by late 2009 Revenu Quebec would begin testing an anti-fraud device - the sales recording module (SRM) - in the restaurant sector. The SRM is designed to detect the erasure of digital sales records in electronic cash registers and point-of-sale systems - a type of fraud that contributes to more than $425 million annually in lost tax revenues in the restaurant sector alone. Quebec studies indicate that restaurateurs are increasingly employing technology to alter digital records in order to conceal income from the business and avoid reporting and remitting taxes due. The SRM will assist provincial auditors in detecting such fraudulent activities. Revenue authorities around the globe have taken two approaches to assuring the integrity of business records in cash-intensive industries: one approach secures the till; the other relies on principles of compliance and enforcement to encourage good business practices. With the introduction of the SRM, Quebec is taking steps to become a fiscal till jurisdiction. This article considers the SRM in a comparative context. The technological approaches of Germany and Greece (both of which are fiscal till jurisdictions) are contrasted with the approach adopted in the Netherlands (a principles-based jurisdiction), which relies on intensive technology-based audits to assure digital record accuracy. The article concludes with a suggestion that there may be something to learn from the US streamlined sales tax initiative, which employs government certification of tax technology to ensure the accuracy of transaction tax determinations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".