A Taste of Tradition: Exploring Geographical Indications and Indigenous Agricultural Knowledge in Canada
Bibliographic record
Abstract
This thesis offers a critical analysis of the use of geographical indications (GIs) to protect food and agricultural traditional knowledge products, with a focus on Canada. The aim of this research is to contextualize questions on the use of GIs within the general framework of international and national regimes governing intellectual property rights, traditional knowledge and agriculture. It also examines the relationship between geographical indications and development in Canada's intellectual property landscape. While the analysis presented in this dissertation is specific to Canada, the insights gained from this research may be relevant to other jurisdictions considering the use of geographical indications as a means of promoting local economic development and protecting traditional knowledge related to food and agriculture. This thesis conducts a comprehensive examination of GIs through doctrinal and comparative study, focusing on the handling of GIs in the USA, EU, and India. The research aims to identify the similarities and differences in their respective approaches, shedding light on the nuanced treatment of GIs in these jurisdictions. The historical context and legal frameworks of GIs in each region, with a focus on the protection of food and agricultural traditional knowledge are examined. Additionally, the disputes that often arise due to the divergent approaches of the EU and the US regarding the form and philosophical foundations of GI protection will be addressed. This thesis also investigates the potential of GIs in meeting the needs of Indigenous peoples and promoting traditional agriculture practices. Key challenges and opportunities associated with using GIs to promote Indigenous agriculture and traditional knowledge will be explored, particularly in the context of existing intellectual property laws and global trade agreements. It is found that in order to making GIs work for the benefit of Indigenous communities in Canada, it would have to be a collaborative effort between Indigenous peoples and federal, provincial and local government. The current intellectual property policies and legislation would have to be re-evaluated and reformed to ensure compatibility with Indigenous domains. ?
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.030 | 0.023 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".