A Ride With My Best Friend: The Fiscal Arbitrators Pseudolaw Tax Evasion Scheme, Recruitment, and Litigation
Bibliographic record
Abstract
Fiscal Arbitrators was a comparative short-lived Canadian pseudolaw tax avoidance scheme that operated between 2006-2012. Taxpayers made “Strawman Theory” claims to create large but fictitious business expenses. Taxpayers who employed Fiscal Arbitrators techniques had no legal basis for their claims. The Canada Revenue Agency assessed “gross negligence” penalties in addition to other automatic charges. At least 500 Fiscal Arbitrators customers appealed their taxation re-assessments at the Tax Court of Canada. There, these taxpayers usually claimed their actions, and blatantly false tax returns, had a reasonable basis. This unusual confluence of factors resulted in a substantial number of written court decisions that include first-hand, first-person, accounts of how and why Fiscal Arbitrators’ customers were recruited, and that describe this “Detax” scheme’s operation. Unexpectedly, Fiscal Arbitrators customers were primarily recruited via person-to person contacts and through family, social, and workplace networks. No Internet based recruitment was reported. Fiscal Arbitrators customers showed little to no interest in or understanding of the basis for their extraordinary claims. Their sole motivation was greed. These taxpayers were mainly non-ideological “mercenaries” who abandoned pseudolaw to conduct damage control steps as rational self interested actors. Most voluntarily terminated their appeals prior to a full appeal court hearing. The characteristics of this study’s Fiscal Arbitrators population do not correspond with how legal, media, and academic sources stereotypically portray and describe pseudolaw adherents. This investigation thus illustrates pseudolaw’s users are potentially more diverse than is commonly recognized.
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 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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".