A Principled Framework for Assessing General Anti-Avoidance Regimes
Bibliographic record
Abstract
The enactment of general anti-avoidance regimes (GAARs) in domestic legislation along with the analysis of those regimes is well documented with academic studies focusing on an examination of the introduction and subsequent interpretation and application of GAARs in jurisdictions such as Australia, New Zealand, Canada, South Africa, the UK, China and India. However, a review of current literature reveals that different academics evaluate GAARs based on different sets of criteria. This results in an absence of a common theoretical framework with which to assess the effectiveness of existing and proposed GAARs. Further, it is arguably difficult to undertake a comparative analysis of GAARs as a lack of a common normative framework decreases the validity and robustness of a comparison of findings. The aim of the study discussed in this article is to develop a normative framework with which to assess GAARs by adopting a thematic analysis of relevant academic articles published since 2000. Thirty-eight academic articles were selected for this study with each article coded to ascertain common themes. This has allowed categories of quotations to be determined and a structured theoretical framework to be developed. The article concludes that the resulting framework highlights five principles relating to the structure and evaluation of GAARs: purposive and objective interpretation, a proactive stance, discretion, certainty, and ability to alter liability. Each of these principles is discussed and, in doing so, this article fills a gap in the current literature by developing a principled framework to help ensure that future studies evaluate GAARs from a single point of view. Finally, the article examines this principled framework in the context of ten GAARs in the Asia-Pacific region in order to see whether governments in that region adopt the factors which have been determined to be part of the framework for a model GAAR.
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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 | 0.256 | 0.184 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.035 | 0.016 |
| Science and technology studies | 0.008 | 0.068 |
| Scholarly communication | 0.022 | 0.022 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".