Evaluating Responses to Offshore Tax Evasion: A Comparative Analysis of Legislative Reforms in the USA and Canada Post-Panama Papers
Bibliographic record
Abstract
Following the revelations from the Panama Papers and other significant leaks, there has been a marked increase in public calls for nations to address tax evasion more effectively. This period has witnessed an unparalleled expansion in global cooperation regarding tax matters, leading to significant strides toward mitigating offshore tax evasion. In this evolving landscape, the focus shifts to North America, where both Canada and the United States have stepped up their efforts to curb tax evasion. They have updated their legal frameworks, enhanced enforcement tactics, and strengthened tax authorities' investigative capabilities. This research delves into the legislative actions taken by the USA and Canada in response to the challenges of offshore tax evasion, employing a comparative analysis to scrutinize the effectiveness and hurdles of these measures. By examining a wide array of sources, including literature, policy documents, and reports, the study aims to evaluate how both countries have revised their tax laws and introduced new strategies to tackle tax evasion. The emphasis will be on assessing the impact of these legislative changes on curbing offshore tax evasion and identifying the challenges in enforcing these laws. This investigation seeks to shed light on the comparative effectiveness of the strategies deployed by the USA and Canada, offering valuable insights into addressing a critical issue in international finance and tax regulation. This paper also provides limitations of the literature evidence for evaluating tax evasion and a research guideline for further work to be performed in this area of study. To enhance the practical understanding of the key points discussed in this thesis, a detailed case study has been prepared and included in Appendix A.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".