RUSSIA-UKRAINE CONFLICT: INSIGHTS ON IMPLICATIONS OF WAR FOR BUSINESSES
Bibliographic record
Abstract
Ongoing conflict between Ukraine and Russia is one of the most pressing issues for international businesses. The topic is gaining enough attention from mainstream business press, but there is no much academic work on the issue from Russia Ukraine conflict in 2022. To address the gap, this review article addresses the impact of Ukraine crisis on the firms operating in Russia and Ukraine and on the global industries considered to be highly affected in war. Research design adopted for the review paper is exploratory research that is built on analysis of secondary data for a period of three months following the war, i.e., February to April, 2022. Sources of the data includes e- newspaper, scholarly articles, relevant government and non-government publications and expert interviews. Evidence from literature indicates that the war has a lethal effect on businesses which belongs to or are trading with Russia and Ukraine but the entire world has started to feel discomfort of the war as major global industries are suffering due to crisis. The study contributes as a conceptual foundation to the knowledge on implications of geopolitical crisis on international businesses and can be utilized to link them to the next major crisis world will have to face.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".