Policy Forum: GAAR Revisited—A Road Map for Continued Analytical Rigour
Bibliographic record
Abstract
New interpretive issues arise from the amendments to the general anti-avoidance rule (GAAR), including the introduction of a novel preamble and an economic substance test, as well as the potential for a penalty to apply where abusive tax avoidance is found. In this article, the author provides a litigator's perspective on how these issues might be addressed by the courts. Although the amendments have been justified on the basis that they were intended to "modernize" GAAR, they serve in large part to codify existing principles that have been carefully developed through years of jurisprudence. The author emphasizes that the courts should rely on those principles to ensure that the GAAR analysis is undertaken with the same analytical rigour as has been judicially developed to date. Moreover, given the potential for a substantial penalty to be imposed where GAAR is found to apply, courts should insist on a stronger evidentiary foundation and a higher standard for a finding of misuse or abuse.
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 | 0.132 | 0.134 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.017 | 0.078 |
| Scholarly communication | 0.051 | 0.061 |
| Open science | 0.011 | 0.013 |
| Research integrity | 0.050 | 0.056 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".