GAAR in Action: An Empirical Study of Transaction Types and Judicial Attributes in Australia, Canada, and New Zealand
Bibliographic record
Abstract
The authors report the results of an empirical study on the general anti-avoidance rules (GAARs) in action in Australia, Canada, and New Zealand. The study builds on a conceptual framework, developed by Tim Edgar, that classifies tax-avoidance transactions as falling into three types (tax-attributes creation, tax-attributes trading, and tax-attributes substitution) and considers the transaction types in connection with the attributes of judges and with the broader context of judicial decision making. To contextualize the empirical analysis, the authors provide a doctrinal analysis of both the countries' GAAR provisions and the judicial interpretation of GAARs, along with some examples of divergence and convergence among the three countries. The statistical results provide some modest support for Edgar's claim that the judiciary's institutional competence is limited when it comes to identifying tax avoidance in substitution cases and that Canada's GAAR could be improved through the incorporation of an economic substance test.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".