Cross Border E-commerce and the GST/HST: Towards International Consensus or Divergence?
Bibliographic record
Abstract
In February 2001, the OECD issued a draft report on ‘‘Consumption Tax Aspects of Electronic Commerce’’. The purpose of this report was to seek comments on Working Party No. 9’s conclusions and recommendations in respect of the approach to be taken on the application of consumption taxes to e-commerce in light of the Ottawa Taxation Framework Conditions. The 1998 Conditions called for the taxation principles that applied to traditional commerce to be the guide for the taxation of e-commerce, to ensure non-discriminatory tax treatment of electronic commerce transactions. In November 2001, the Canada Customs and Revenue Agency (‘‘CCRA’’) issued its own discussion paper in respect of the application of GST/HST to electronic commerce. By July 2002, the CCRA was able to issue its formal views on this issue, with the publication of its GST/HST Technical Information Bulletin. The purpose of this article is to review the position taken by the CCRA in the GST Bulletin insofar as it relates to the application of the GST/HST to cross-border electronic commerce transactions, and to assess how the position taken by Canada stacks up to the principles set out in the OECD Draft and the position being formulated by certain other major OECD members.
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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 | 0.021 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".