Climate change and<i>terroir</i>: The challenge of adapting geographical indications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The concept of terroir is 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 of terroir such 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 on terroir . These factors raise questions as to how conceptions of terroir and the formalized rules underpinning the distinctiveness of GIs are evolving in the face of climatological changes. This paper discusses how climate change may influence how terroir is 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 of terroir . It then explores three options for products with GIs based on terroir that are experiencing climate change: product quality change, definitional change, or re‐interpreting the boundaries of terroir relevant to the GI distinction.
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.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it