E-Commerce or Digital Trade? Why the Difference Should Matter To Trade Lawyers
Bibliographic record
Abstract
This chapter argues that trade law(yers) should draw systematic distinctions between digitally enabled trade ("e-commerce") and the exchange of purely digital assets ("digital trade"). They cover fundamentally different transaction types, give rise to distinct policy concerns, and are subject to different rules. E-commerce does not change what is traded (goods and services including their digital avatars) but how it is traded (online). This "eBay"-type trade falls within existing trade paradigms and is subject to globally converging domestic and international rules. In contrast, trade in digital assets (e.g. models, algorithms, data) may not be trade, its exchange being often gratuitous. This "data economy"-type trade falls outside existing trade paradigms and is subject to only emerging and often conflicting national and international rules. The chapter draws a line between digitally enabled trade and trade in digital assets to counter existing trends to conflate the two spaces, empirically shows how rulemaking in trade agreements has increasingly moved from one to the other, and discusses the advantages of their analytical separation in the context of ongoing rulemaking in free trade agreements and at the World Trade Organization.
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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 | 0.018 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.031 |
| Scholarly communication | 0.013 | 0.029 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.021 | 0.018 |
| Insufficient payload (model declined to judge) | 0.030 | 0.005 |
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".