POLISH FOREIGN TRADE OF AGRICULTURAL AND FOOD PRODUCTS DURING THE COVID-19 PANDEMIC
Bibliographic record
Abstract
In the research on the impact of the COVID-19 pandemic on the economic situation in agriculture, the demand and supply channels are distinguished. They indicate how restrictions on economic activity translate into the volume and structure of production and the demand for agricultural products. The aim of the research was to identify and assess the impact of the COVID-19 pandemic on Polish agriculture through the transmission channel of foreign trade. The research period covered the years of 2017-2022. The primary research tool used was time series indicator analysis. During the pandemic, changes in foreign trade were limited only to short-term disruptions, which intensified in the first wave of COVID-19 (2020, second quarter). Trade in agri-food products turned out to be more resistant to shocks caused by the pandemic compared to trade in non-agricultural sectors. Therefore, disruptions on foreign markets did not significantly affect the production and economic situation of Polish agriculture. In the second quarter of 2020, the value of exports of agri-food goods decreased by 2.8% compared to the previous quarter. As it comes to other groups of goods, export values were lower by 7.2-40.1%. At the same time, the value of imports of agri-food goods was lower by 6.1% compared to the previous quarter. Imports of other goods collapsed much more severely as decrease in the value of imported goods ranged from 8.4 to 47.4%.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".