International Trade of Agricultural Products in Disruptive Times – The Correlation between Exports Subjects
Bibliographic record
Abstract
International trade helps reduce food insecurity by connecting the regions with limited agricultural potential and large populations to the regions with comparative advantages in agriculture. The international trade of agricultural products appeared to be vitally important in times of such challenges of nowadays as the COVID-19 pandemic, climate changes, turbulences on the political scene, etc., which the whole humanity has to face and overcome. The purpose of the article is to assess if the exports of the agricultural products from Ukraine to the EU and from Canada to the EU are correlated and, if they are, how strong the correlation is. The data under analysis are the export amount of goods from the Standard International Trade Classification (SITC) groups 0, which comprises food and live animals, and 1, which contains beverages and tobacco. The timeframe under analysis is 10 years – from 2011 to 2020 included. Such simple statistics of the data sets under analysis as mean, standard deviation, sum as well as minimum and maximum values were calculated and compared. The dynamics, yearly changes and general trend lines of the data sets under research were analysed and compared. The general trend lines of the data under analysis were built and the projections for the following two periods were made in the article using the appropriate functions, having chosen from the exponential, linear, logarithmic, polynomial and power ones, taking into consideration the values of R² coefficients. The analyses for the data normality distributions were conducted. The Pearson and Spearman correlation coefficients as well as their p-values of the data researched were calculated and analysed. The research itself as well as its results would be interesting and useful for the public administration officials, business people, decision-makers as well as beginners and experienced specialists in data analysis and statistics.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".