Frames and images facing Ukraine: comparing Germany's and Russia's media perceptions of the EU relations with Ukraine
Bibliographic record
Abstract
The recent EU-Ukraine Summit in July 2018 demonstrated that the leaders of the EU and Ukraine have committed to further deepen the political association and economic integration of Ukraine with the EU. Yet, this “strong partnership,” based on a joint association agreement, has been overshadowed by Russia’s illegal annexation of Crimea and its instigation of the war in Donbas. Given that Ukraine is an important geopolitical neighbour for both the EU and Russia, the EU and its Member States – especially Germany and France – have taken on the role of mediators in the Russia-Ukraine conflict. The focus of our study is on the image of the EU-Ukraine relationship as a unique and outstanding case. Ukraine’s close ties with Russia appear to be waning, however, the more Ukraine tries to strengthen its ties with the EU, the more Russia seems to resist. In this regard, we ask: How are the relationships between the EU and Ukraine are represented in German and Russian print media? How do the print media sources frame this relationship and what different images do they communicate? The content analysis of data draws diverging pictures: within the same period, the patterns of interaction between the EU and Ukraine, evolving within European Neighbourhood Policy and Eastern Partnership, tend to be depicted as far more cooperative in the German press, whereas Russia’s print media portray EU-Ukraine relations as increasingly negative and more conflicted over the years.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".