Media, politics, and Jewish migration from East Europe amid the military crisis in Ukraine, 2014–2015
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it