Transforming the Internet into a Taxable Forum: A Case Study in E-Commerce Taxation
Bibliographic record
Abstract
This Article discusses how (and why) governments should strive to use Internet technologies to protect their ability to collect taxes.The need for effective regulatory action is particularly acute in the tax area because more and more commercial activity is taking place over the Internet.1 0 Regulators, however, have not yet devised a way to effectively tax the exploding e-commerce industry.1 ' This inability to effectively tax Internet transactions will eventually lead to significant revenue losses for local, state, and federal governments, perhaps resulting in the inability to fund important public goods such as schools, roads, or hospitals.12 The need to ensure that the Internet does how great social tasks-like the construction of the Internet-can only be sustained through great social energy and wise choices); Joel R. Reidenberg, Governing Networks and Rule-Making in Cyberspace, in BORDERS IN CYBERSPACE 84, 96 (Brian Kahin & Charles Nesson eds., 1997) ("Policy makers must begin to recognize network sovereignty and begin to shift the regulatory role of states toward indirect means that develop network rules."). 10.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".