Bibliographic record
Abstract
This paper focuses on taxation issues faced by non-resident e-commerce companies with no sustained presence in Canada apart from a web site. The tax liability of foreign corporations with a Canadian subsidiary, a physical Canadian office, or Canadian-based employees or agents will not be considered, even though there is substantial overlap in some of the relevant issues. By e-commerce companies we refer broadly to any firms conducting their primary business — whether business- to-business (B2B) or business-to-consumer (B2C) — by means of the internet.\nIn the first section we outline the framework for Canada’s taxation of non-residents conducting business in Canada and introduce the value-added taxation of goods and services supplied within the country. Second, we consider one of the most difficult issues that has arisen in connection with the taxation of e-commerce businesses, namely, the characterization of income from e-commerce transactions for tax purposes. The third section looks at Canada’s taxation of a non-resident e- commerce firm’s business profits under Part I of the Income Tax Act and relevant treaty provisions. Fourth, we examine tax liability under Part XIII of the ITA, which concerns withholding tax on certain categories of payments made to non-residents. Finally, in section five we discuss administrative requirements with respect to the taxation of goods and services imposed on non-resident firms conducting e-commerce in Canada.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".