The Consequences of the Military Conflict in Eastern Ukraine, Its Impact on International Investment Attractiveness, Economic and Demographic Development in Ukraine
Bibliographic record
Abstract
The conflict in the Eastern part of Ukraine and the growing geopolitical tensions have had a significant impact on the economy and society of the country. As a result, it deepened the recession and diverged from the planned development indicators. In particular, this concerns international reserves of the National Bank of Ukraine and the country's budget deficit. Multilateral economic changes, exacerbated by the impact of hostilities in the Eastern part of the country have transformed the structure of socio-demographic processes in Ukraine. Armed confrontation causes a continuous deterioration of demographic and economic indicators of development not only of Donetsk and Luhansk regions, but also has an impact on the whole country. This confrontation is also accompanied by the loss (destruction, theft, etc.) of public assets. The estimated cost of destroyed components of industrial, communal, social, transport, energy and other infrastructure are indicative due to the inability to inspect objects located within the territory controlled by terrorist groups. The conflict has also affected the investment attractiveness of the country, which accelerates the creation of a depressed nature of country’s development. Therefore, in the context of hostility in the Eastern Ukraine, it is important to understand and study its destabilizing impact, not only on domestic economic and demographic indicators, but also on the volume of foreign investment, which will allow us to understand the level of country’s involvement in the global investment space and the real impact of military action on the population and on international economic affairs of Ukraine. As a result of this scientific research, the population and GDP forecast have been conducted. It is worth noting that the forecast itself based on regression mathematical modelling which includes past data and can be accurate if current conditions are stable in the future.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".