The Media Image of the Multinational Transcarpathia: The Problems of the Search for Ethnic and Civil Identities
Bibliographic record
Abstract
The article is concerned with the media image of Transcarpathia in the light of the formation of ethnic and civil identities of the auditory of Ukraine's westernmost region. The peculiarity of this region as a part of the national information space, lies within the fact that the local mass media satisfy the information needs not only in Ukrainian, but also in Hungarian, Romanian, Russian, Romani, German, Slovak, and other languages. At the same time while residing in the borderland area and speaking several languages, the Transcarpathian audience has free access to any media product of the four neighboring EU countries. The broad palette of national and non-national media is called to facilitate the formation of the Transcarpathians' affiliation not only to their ethnic group, but also to the civil (national) identity. To the author's mind, however, an intentional separatist image of the region is emerging due to the destructive materials, the spreading of information myths both in Ukrainian and foreign mass media. The resolution of these problems is seen by the author in the creation of a distinct media system and development of a national information strategy in Ukraine and in the multi-ethnic borderland areas among others.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".