Bibliographic record
Abstract
This paper considers the operation of general anti-avoidance rules (‘GAARs’) in similar common law jurisdictions such as Australia, New Zealand, Canada and the United Kingdom to ascertain whether there has been a noted trend of convergence in the way these GAARs operate. This paper concludes that there has been a noted trend towards convergence as it now appears to be the case that, no matter what the specific wording of the GAAR actually is, the enquiry undertaken by the courts in common law jurisdictions is effectively the same. Relevantly, this enquiry looks to the overall purpose and structure of the transactions at issue and whether they lack any real commercial substance. This conclusion may seem contrary to prevailing attitudes about statutory interpretation, however, the evidence reveals that no matter what specific wording might be adopted in the GAAR, the identification of tax avoidance as involving artificially contrived, complex arrangements that produce no real economic substance, is applied in almost the exact same way across the different jurisdictions reviewed. This trend towards convergence indicates that the reviewed GAARs operate in largely the same ways. It is, however, acknowledged that whilst the enquiry undertaken is generally the same across the different jurisdictions examined, there are, nevertheless, differences in the outcomes possible due to different thresholds being applied to determine where the line of artificiality and tax avoidance is deemed to exist.
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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 | 0.022 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".