Ouverture des marchés et localisation des productions agricoles Market openness and location of agricultural activities
Bibliographic record
Abstract
Nous analysons dans cet article l'impact de l'ouverture des marchés sur la localisation des activités agricoles et agro‐alimentaires entre deux régions représentant de manière stylisée l'Union européenne et le reste du monde. L'analyse repose sur un modèle d'économie géographique qui considère certaines caractéristiques de l'agriculture et des marchés de produits alimentaires. Dans un cadre de marché en concurrence monopolistique où les produits sont différenciés horizontalement et verticalement, il apparaît que l'ouverture du marché induit dans tous les cas une baisse de la production communautaire. Seule une forte différentiation des produits selon leur origine géographique permet de limiter cette baisse. We analyze, in this article, the effect of a liberalization of the markets on the location of agricultural and agro‐food activities, between two stylized regions: the European Union and the rest of the world. We build an economic geography model. It considers the specificity of the agricultural activity and the food markets. In a monopolistic competition framework where the products are horizontally and vertically differentiated, we show that the openness of the market induces a decrease of the production in the European Union. Only a strong differentiation of the production according to its geographic origin can limit this effect.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".