Product Qualification as a Means of Identifying Sustainability Pathways for Place-Based Agri-Food Systems: The Case of the GI Corsican Grapefruit (France)
Bibliographic record
Abstract
Existing frameworks offer a holistic way to evaluate a food system based on sustainability indicators but can fall short of offering clear direction. To analyze the sustainability of a geographical indication (GI) system, we adopt a product-centered approach that begins with understanding the product qualification along the value-chain. We use the case of the GI Corsican grapefruit focusing on understanding the quality criteria priorities from the orchard to the store. Our results show that certain compromises written into the Code of Practices threaten the system’s sustainability. Today the GI allows the fruit to be harvested before achieving peak maturity and expectations on visual quality lead to high levels of food waste. Its primary function is to help penetrate mainstream export markets and to optimize labor and infrastructure. Analyzing the stakeholders’ choices of qualification brings to light potential seeds for change in the short run such as later springtime harvests, diversification of the marketing channels, and more leniency on the fruit’s aesthetics. These solutions lead us to reflect on long-term pathways to sustainable development such as reinforcing the fruit’s typicality, reducing food waste, reorganizing human resources, and embedding the fruit into its territory and the local culture.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".