Designing Tax Policy for the Digital Biosphere: How the Internet is Changing Tax Laws
Bibliographic record
Abstract
In order to promote a better understanding of the relationship between tax laws and Internet transactions, this Article integrates tax policy discussion with cyberlaw theory by developing a theoretical legal model called the biosphere. The digital biosphere is the Internet, an online forum characterized by internal diversity and technological evolution where commercial and non-commercial activities overlap to a certain extent. The role of tax law, it is argued, is to protect real world norms (e.g., the desire to maintain neutral tax treatment between traditional commerce and e-commerce) by aligning tax laws with the nature of the network without unduly interfering with emerging cyberspace norms (e.g., transactional anonymity). On the one hand, reform efforts such as the state-sponsored Streamlined Sales Tax Project (SSTP) are properly addressing the challenges presented by Internet commerce by advocating an Internet-based automated sales and use tax collection system, the extension of state and local tax jurisdiction over remote e-commerce vendors, and the unification of different state and local tax bases. On the other hand, other reform efforts such as the new OECD rule to tax computer server/permanent establishment profits for international income tax purposes fails to address these challenges.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".