Gladwellian Taxation: Deterring Tax Abuse Through General Anti-Avoidance Rules
Bibliographic record
Abstract
To reduce tax evasion, most nations use either common law anti-abuse doctrines, statutes, administrative rules, or a combination of the three. None of them have worked fully, and, given human nature, it is unlikely that anything will ever work fully. However, as this Article will outline, some solutions are more effective than others. This Article will propose a blended solution, using the concept of power-law distributions and their extension into social science. The first Section of this Article defines tax abuse and avoidance, distinguishing the two terms. The next Section details general anti-avoidance rules (GAARs), highlighting their flaws and strengths. It explains the history of one of the world's oldest GAARs, the Canadian GAAR. Finally, this Article will propose a new system, arguing that any true attack on tax avoidance or abuse must be aimed at the tax avoidance mentality, not the tax shelter de jour. I refer to this new system as the GAAR-plus. It is a system of targeted cooperative enforcement, where taxpayers designated as tax avoiders, will work with IRS agents both prior to and after transactions, in an effort to alter the chronic tax avoidance mentality. The GAAR-plus system is based on prior Treasury success with cooperative enforcement, but draws heavily upon Malcolm Gladwell's writings on power law distributions for inspiration. Tax shelters run in fads; an outlawed fad may vanish but the desire to reduce one’s tax bill has not been managed in the slightest.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".