'To GAAR or Not to GAAR — That Is the Question:' Canadian and Australian Attempts to Combat Tax Avoidance
Bibliographic record
Abstract
\n\t\t\t\t\tIn both Canada and Australia the relevant governments found their initial legislative attempts to combat tax avoidance to be ineffective. In time in each country it was concluded that the respective general avoidance provisions were of limited application and avoidance provisions were of limited application and ineffective to combat the sophisticated tax avoidance schemes promoted by tax advisers. In Canada it was determined that Income Tax Act, R.S.C 1985, s. 245(1) would be repealed and replaced with a general anti-avoidance rule ('GAAR') contained in a new s. 245 ITA. The Australian government similarly decided to replace Income Tax Assessment Act, Cth. 1936, s. 260 with a new general anti-avoidance measure, Part IVA ITAA. This article compares and contrasts the Canadian and Australian GAARs. Through the evaluation of each regime the article seeks to identify which model is most effective. It will be sen which model is most effective. It will be seen that both regimes have some features that are preferable to the other and thus both GAARs might be improved by incorporating aspects of the other anti-avoidance model.\n\t\t\t\t
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".