To what extent the non-Extension of the Black Sea Grain Deal is Disrupting Globaland Arab Wheat Markets
Bibliographic record
Abstract
The ongoing conflict between Russia and Ukraine has significantly affected the global wheat market, particularly impacting Arab countries that heavily rely on wheat imports. This paper examines the conflict's effects on wheat production and exports, highlighting disruptions in Ukraine and resulting price volatility. Together, Russia and Ukraine account for a large share of global wheat exports, but the conflict has led to a decline in Ukrainian exports, mitigated somewhat by the Black Sea agreement that allowed for continued exports despite Russian sanctions. As major exporters like the U.S., Canada, and Australia step in, competition has intensified, leading to fluctuating prices. This volatility threatens food security and fiscal stability in Arab nations, especially those with limited or no wheat subsidies. The study suggests that the nonrenewal of the Black Sea agreement could raise global wheat prices by 3-4% on average, though the overall impact is expected to be short-lived due to the market's resilience. The findings emphasize the need for proactive import planning and the importance of agricultural policies and trade finance in shaping wheat market dynamics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".