Electronic Commerce and Multijurisdictional Taxation
Bibliographic record
Abstract
Electronic Commerce and Multi-Jurisdictional Taxation (2001) is the successor to the widely-acclaimed Electronic Commerce and International Taxation (1999). The new edition contains expanded and enhanced consideration of the tax treatment of electronic commerce from both an income tax and a consumption tax perspective. Not only does the new edition provide a detailed and up-to-date analysis of VAT developments regarding e-commerce, but it also explores the implications of e-commerce for the US state and local sales and use tax regime. The new edition discusses developments in Europe and the United States while enlarging its focus to include the tax treatment of e-commerce in China, India, Canada, Australia, and throughout the world. At the same time, the authors have deftly woven the latest OECD and European Community developments into the fabric of the book. There is no other book on the market today that analyzes the practical tax consequences of e-commerce with the multi-jurisdictional and multi-tax perspective of this insightful work by distinguished academics and practitioners Richard Doernberg, Luc Hinnekens, Walter Hellerstein, and Jinyan Li.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".