Identification and Levelling of Crisis Phenomena in the World Grain Market in the 2022/23 Marketing Year
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.
Bibliographic record
Abstract
Abstract The aim of this paper is the detection crisis phenomena in the world grain market in the 2022/23 marketing year (MY), which worsened during the Russia-Ukraine invasion on February 24, 2022. Some countries and international organizations have recently expressed concern that the reduction of grain supply on the world market and the rapid rise in its price. Whether the impact of the destabilizing situation on the world grain market on the exacerbation of hunger is an open question. In order to fill the research gap, the paper tries analysis the global market into grain types between 2008/09 MY to 2021/22 MY and identify on it the shares of Ukraine and Russia. Two methods are used to conduct a comprehensive study of the grain market - fundamental and technical analysis. The analysis of the state and dynamics of the main indicators of the world grain market was carried out with the help of fundamental analysis. The results show that the volume of grain production in the world and the two warring countries are growing. Both countries supply about a quarter of all products in the overall structure of world grain exports. Finally, the result also shows that Ukraine and Russia are key exporters of barley, rye, wheat, and corn to low-income and least developed countries. Grain price forecasting through technical analysis was carried out. Based on the results obtained during the fundamental and technical analysis, three scenarios for the development of the grain market and its impact on the problem of hunger were proposed and given recommendations for levelling of crisis phenomena.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it