The Effect of Hungarian Kin-State Politics on the Economic Life of Transcarpathia, Ukraine: A Snapshot of the Beregszász District between 2017 and 2020
Bibliographic record
Abstract
Abstract Due to the frequent changes in ruling powers and shifting borders, in addition to the geopolitical situation of the region, Transcarpathia, the westernmost county of Ukraine, constitutes an especially suitable site for examining transnational ethnic kinship relations. The main aim of the present article is to shed light on the correlation between social-ethnic interactions and economic efficiency, paying special attention to the role of informal and formal ethnic relations in the activities of business actors and in their access to certain resources. The article analyzes how these processes have been influenced by Hungary’s kin-state politics. The research is based mainly on semi-structured interviews conducted with local Transcarpathian Hungarian and Ukrainian entrepreneurs as well as with the representatives of business organizations related to the given community and experts in the field. The investigation found that among Transcarpathian Hungarian entrepreneurs the role of formal ethnic relations has strengthened due to the financial support provided by Hungary, which was not the case for entrepreneurs belonging to the majority population. The extensive assistance by Hungary has given Hungarian entrepreneurs an advantage in accessing resources, which has caused some tension between Hungarian and Ukrainian entrepreneurs in Transcarpathia.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".