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Record W7083578658 · doi:10.1016/j.xinn.2025.101124

Global soybean trade dynamics: Drivers, impacts, and sustainability

2025· article· en· W7083578658 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Innovation · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeological Research and Protection
Canadian institutionsMinistry of AgricultureUniversity of Toronto
FundersChinese Academy of Agricultural SciencesChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsSustainabilitySupply chainTrade barrierFood systemsCommercial policySustainable developmentPerspective (graphical)

Abstract

fetched live from OpenAlex

Since the 20th century, the global soybean trade has undergone major changes, shaped by rising demand, climate-related risks, and shifting international dynamics. Despite its global importance, important gaps remain in understanding the complex drivers and sustainability challenges of this transformation. This review synthesizes both direct and indirect forces reshaping trade flows, spanning market dynamics, supply chain logistics, policy shifts, and technological innovation. We examine how soybean trade expansion has impacted deforestation, inequality, and food security, and assess the responses of governments and companies to address these challenges. Finally, we provide a forward-looking perspective on the strategic pathways needed to ensure a more resilient and sustainable global soybean system. The integrated insights offered in this review can inform sustainable trade strategies and foster cross-scale policy coordination for a more resilient global agri-food system.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.454
Threshold uncertainty score0.236

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.272
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it