Private Interests, Public Borders, and the NAFTA's Chapter 11: Lessons from the Mad-Cow Saga
Bibliographic record
Abstract
This article provides a detailed overview of the mad-cow saga that took place from 2003 until 2005, and discusses its adverse impact on the economic markets of Canada and the United States in terms of trade synergies and amicable commercial relationships. The authors go on to discuss the remedies and dispute resolution mechanisms offered by the NAFTA, particularly c. 11. Ultimately, the article argues that c. 11 is a useful tool for parties to redress their loss when they have been subjected to unfair treatment by interest groups that pursue litigation in domestic courts. This argument is grounded in a discussion of Leowen v. O’Keefe and the litigation pursued by Ranchers Cattlemen Action Legal Fund.
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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.029 |
| Scholarly communication | 0.018 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".