Bibliographic record
Abstract
In much of the legal literature, the fact that the United Nations Convention on Contracts for the International Sale of Goods (CISG) has been ratified by so many nations constitutes incontrovertible evidence of its success. This narrative fails to account, however, for the fact that private parties can choose to exclude the CISG from their international sales contracts. This Article draws upon a hand-collected dataset of contracts executed by public companies in Canada to show that these companies overwhelmingly choose to exclude the CISG from their international sales agreements. It also shows that these same companies are frequently unaware that selecting the law of a Canadian province can result in the application of the CISG and that few (if any) of these companies consciously select the CISG by selecting provincial law. While the Article turns up a few tantalizing hints that attorneys practicing in Quebec may be slightly more receptive to the CISG than attorneys practicing in the rest of Canada, the overall portrait that emerges is of a nation where this treaty is excluded by sophisticated actors in almost all cases. This finding raises important questions of whether the CISG is achieving its intended purpose of facilitating international trade.
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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.026 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".