Bibliographic record
Abstract
On September 5, 2023, the Comprehensive and Progressive Agreement for the Trans-Pacific Partnership Panel (CPTPP Panel or the Panel) issued its first decision. The case was initiated in May 2022 by New Zealand which claimed that Canada's system for the administration of its tariff rate quotas on dairy projects is inconsistent with Canada's obligations under the Partnership Agreement. After consultations with Canada failed, New Zealand requested that a panel under Article 28.7 of the Agreement be established to examine the issue. There was a dispute about the role to be played by a prior decision of the US-Mexico-Canada Agreement (USMCA) Panel on similar issues. New Zealand felt that the USMCA Panel decision was “highly pertinent” because of the similarities between relevant provisions in the two agreements, but Canada disagreed, arguing that not only is the USMCA decision irrelevant but that its interpretation of the relevant provisions was incorrect. Australia, which intervened as a third party participant, agreed with New Zealand, pointing to the need to ensure consistent decisions concerning what it deemed were identical provisions in the two agreements. Japan, another third party participant, suggested that the panel ensure that its decision was made in reliance on Articles 31 and 32 of the Vienna Convention on the Law of Treaties (concerning rules of interpretation).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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; both teacher heads agree on what is shown here.
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".