The Synergy of Tradition and Innovation Leading to Sustainable Geographical Indication Products: A Literature Review
Bibliographic record
Abstract
The traditional production of geographical indications (GIs) are struggling to react to external influences such as climate change, changing market conditions. There is a call for innovation within GI products without compromising traditional practices. In GI research, tradition and innovation are often debated because it is apparent that they exclude each other. However, there are findings that a combination of these two elements can have effects on sustainability. Through acknowledging the synergy, diversification strategies are commonly used; those have a remarkable effect on all dimensions of sustainability (social, economic, environmental). The aim of this paper is to show evidence from literature stating that the incorporated tradition of GI products can exist in synergy with innovation. The TISyn (tradition-innovation synergy) model is presented as starting point for future research on this matter. We conclude that focusing on innovation within the GI scheme is required for a changing focus on sustainable productions. However, examples show that without taking tradition into account, GI stakeholders obtain negative outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".