Characterizing the Drivers of Global Food Trade Growth in 21th Century
Bibliographic record
Abstract
21st century is characterised by a steady growth in the global demand for basic foodstuffs. This paper reviews the drivers of this growth, through a descriptive analysis of the main literature on the subject, in order to synthesize the most relevant information generated by researchers and position the current state of the issue. The results of the analysis suggest that emerging economies have taken over in the increase of food imports; this is due to the potential of countries such as China, India, Brazil and Russia, which have become propellers of the global economy. From the developing countries, the increase in population and income are the driving forces behind the dynamism of world food demand, whose direct consequences are the increase in per capita consumption, the acceleration of the urbanization process in these regions and the increase in the consumption of products with greater added value. In developed economies, increases in per capita income do not translate into increases in the demand for food; rather, its role with respect to global demand is to promote it as they deepen the production of biofuels, the liberalization of the agricultural sector and the signing of trade agreements. Finally, the work concludes by warning about the uncertainties that surround the demand for food imports, including the crucial role played by climate change.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".