When It Comes to General Anti-Avoidance Rules, is Broader Better?
Bibliographic record
Abstract
This paper examines the proposition that general anti-avoidance rules achieve their purpose better when drafted in broad terms. Several jurisdictions have included misuse and abuse requirements in their GAARs in order to provide certainty and a high threshold for the GAAR’s operation. Others have enumerated their GAAR to add precision and certainty to its terms. While misuse and abuse requirements and enumeration have the appearance of adding precision to an uncertain area of law, in practice this is doubtful. The general anti-avoidance provisions of four jurisdictions are compared, namely Australia, Canada, New Zealand and the United Kingdom. This article comes to two conclusions; that adding a misuse and abuse requirement to a GAAR does not significantly alter the substance of the inquiry; and that adding further details and precisions to a GAAR does more harm than good. These two conclusions promote the main proposition of this paper, that general anti-avoidance rules work best when drafted in broad terms. The international trend is heading towards more enumerated general anti-avoidance provisions; this paper aims to counter some of the arguments in favour of that trend.
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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 | 0.033 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.016 | 0.025 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".