Geographical Indications, Conflicted Preferential Agreements, and Market Access
Bibliographic record
Abstract
Canada is currently negotiating a Comprehensive Economic and Trade Agreement with the European Union; the issue of geographic indications is on the negotiating agenda and is expected to be one of the most contentious issues in the negotiations. While the exact nature of protection for Geographic Indications to be included in the agreement is not yet clear, there is potential for a conflict with commitments made by Canada in North American Free Trade Agreement. This article explores the wider issues surrounding differences in the protection of intellectual property and the effect on market access as well as the potential specific issues pertaining to the Comprehensive Economic and Trade Agreement for North American Free Trade Agreement members. General issues include, among others, how market access could be restricted either by de facto import bans or the imposition of additional costs on exporting firms; would these restrictions qualify as nullification or impairment of a benefit under the General Agreement on Tariffs and Trade? Does the Trade-Related Aspects of Intellectual Property provides any guidance for this issue and would GIs be treated in the same way as a country entering a customs union and having to provide compensation if it raises tariffs to the common level?
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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".