Bibliographic record
Abstract
At first glance, the recent Canadian litigation over the right to use ‘Demerara sugar’ plays out as a defamation dispute. The Guyanese Minister for Agriculture had complained that the use of ‘Demerara sugar’ by an importer on sugar from Mauritius was misleading, since Demerara took its name from the eponymous Guyanese province. The importer took exception to allegations of misleading use, characterising them as libellous. In these circumstances, the central issue before the trial and appellate courts concerned the existence of a sovereign immunity defence to libel claims. Yet despite being framed by libel law, this dispute represents the surface of deeper crosscurrents relating to international Geographical Indications protection, since the Ministers objections rested on the assumption that ‘Demerara sugar’ should be reserved for Guyanese producers. Unpacking this dispute reveals traces of the two most controversial issues in international GI law today: (1) a conflict between trademarks and GIs, as well as (2) the generic use of a formerly geographical designation. The Demerara sugar litigation therefore provides us with an instructive case study of an attempt to integrate GI protection within a trademark law paradigm, while also highlighting the important qualifications and limitations of this approach.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".