Media, politics, and Jewish migration from East Europe amid the military crisis in Ukraine, 2014–2015
Bibliographic record
Abstract
Over the course of the ongoing war in Ukraine, the identity of the global Russian-speaking Jewish community was put to the test. The conflict in Ukraine marked the first time in the history of Russian-speaking Jews that every expression, blog or Twitter post, and opinion article were recorded on the World Wide Web. This readily available data enables us to reconstruct the information climate that surrounded Russian-speaking Jews. The present article explores the sway of this climate on the political discourse of Jewish elites in Ukraine, Russia, and Jewish Russian-speaking diasporas between 2014 and 2015. Our findings suggest that identities of these groups are multilayered, but not hierarchical. Moreover, the elites’ common ethno-cultural Jewish identity coexists with distinct political affiliations. The allegiance of minorities to host societies is a well-known phenomenon. However, its mechanisms have yet to command sufficient research interest. Is it fear, prudence, genuine attachment to the country of residence, or other factors that stand behind the minorities’ commitment? This paper fuses thematic maps with content analysis to show that the “infosphere” is a key to understanding the position of Jews toward host regimes and their co-ethnics in other nation-states.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".