Climate change and<i>terroir</i>: The challenge of adapting geographical indications
Bibliographic record
Abstract
The concept ofterroiris often included in legal descriptions of Geographical Indicators (GIs). GIs are intellectual property that recognizes a food, beverage, or artisan product as holding distinct properties based on geographic origin. GIs are used to indicate these distinctions while deterring the sale of products carrying similar labels without having the GI determined qualities. Climate change and its effects on aspects ofterroirsuch as rainfall, water availability, soil quality, and temperature is already having an effect on some production aspects crucial to what brings distinctiveness to GI products based onterroir. These factors raise questions as to how conceptions ofterroirand the formalized rules underpinning the distinctiveness of GIs are evolving in the face of climatological changes. This paper discusses how climate change may influence howterroiris encoded in legally recognized GIs and how this will influence international regulation, recognition, and trade flows in GI‐protected food and beverages. It discusses the relationship between GIs, credence attributes and the legal recognition ofterroir. It then explores three options for products with GIs based onterroirthat are experiencing climate change: product quality change, definitional change, or re‐interpreting the boundaries ofterroirrelevant to the GI distinction.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| 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".