Consumer Demand and Cost Factors Shape the Global Trade Network in Commodity and Manufactured Foods
Bibliographic record
Abstract
Multiple forces operate throughout the global economy and influence the direction, composition, and volume of agri‐food trade. The fundamental determinants that impede and foster bilateral trade in two food types, namely staple commodities and manufactured products, are identified using generalized gravity equations. Empirical evidence verified the importance of relative resource endowments and similarities in the structure of partner demand. Other socio‐geo‐political factors were also found to influence food trade, including the ability of governments to control corruption and curtail disequilibrium in financial markets. L'économie mondiale est soumise à des forces multiples qui influencent l'orientation, la composition et le volume du commerce agroalimentaire. Les principaux facteurs qui entravent et favorisent le commerce bilatéral de deux types d'aliments, à savoir les matières de base et les produits manufacturés, ont été déterminés à l'aide d'équations de gravité généralisées. L'évidence empirique a vérifié l'importance des dotations relatives en ressources et des similarités dans la structure de la demande d'un partenaire commercial. D'autres facteurs sociaux et géopolitiques, y compris la capacité des gouvernements à combattre la corruption et à réduire le déséquilibre sur les marchés des capitaux, influenceraient aussi le commerce alimentaire.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".