Bibliographic record
Abstract
Abstract It is a truth universally acknowledged that every ambitious twenty-first century trade agreement is in want of a chapter on electronic commerce. One of the most politically sensitive and technically challenging issues is personal privacy, including cross-border transfer of information by electronic means, use and location of computing facilities, and personal information protection. States are learning to solve the problem of state responsibility for something that does not respect their borders while still allowing twenty-first century commerce to develop. A comparison of the Canada–European Union Comprehensive Economic and Trade Agreement (CETA) and the Trans-Pacific Partnership (TPP) allows us to see the evolution of the issues thought necessary for an e-commerce chapter, since both include Canada, and to see the differing priorities of the US and the EU, since they are each signatory to one of the agreements, but not of the other. I conclude by seeking generalizations about why we see a mix of aspirational and obligatory provisions in free trade agreements. I suggest that the reasons are that governments are learning how to work with each other in a new domain, and learning about the trade implications of these issues.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".