Consequences of russia’s military invasion of Ukraine for Polish-Ukrainian trade relations
Bibliographic record
Abstract
An accurate forecast of interstate trade volume allows for short-term and long-term planning, particularly deciding on state budget revenues, foreign exchange earnings, border arrangement, other infrastructure, migration and social policies. Hostilities are destructive so the russian military aggression against Ukraine in 2022 needs to be assessed in terms of its effects on key economic aspects of Polish-Ukrainian relations, as Poland has been the main economic, trade and social partner of Ukraine in recent years. This article analyses the trade dynamics between the two countries since 2005. It was found that since 2015 the main trends of this dynamics have changed. Monthly data from 2015 to 2021 were used for modelling and forecasting. Relevant SARIMA and Holt-Winters exponential smoothing models were built. These models forecast the volume of trade for the fourth quarter of 2021 and the first quarter of 2022. The relative errors of forecasting (compared to actual data) for October, November and December 2021 were as follows: according to the SARIMA model – 0.8%, 3.6% and 2.3%, respectively; for the Holt-Winters model – 1.9%, 3.6% and 0.7%, respectively. Given the expectations and consequences of russia’s military aggression against Ukraine, the average projected trade turnover between Ukraine and Poland was reduced by 20% per month for the first quarter of 2022. In comparison with the available actual (preliminary) data for January 2022, such a pessimistic forecast gave the following relative forecasting errors: according to the SARIMA model – 3.8%; according to the Holt-Winters model – approx. 1%.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".