Bibliographic record
Abstract
In the first decade after the breakup of the USSR, both local and Western experts believed that Russians and Russian speakers might endanger the social stability or even the territorial integrity of the newly independent states they had found themselves in.Even in countries such as Ukraine, where the Russians seemed to be culturally close to the titular population, most authors did not believe that this minority would put up with nationalizing policies allegedly pursued by the majority-dominated state.The Russians' resistance was considered inevitable in view of their distinct ethnocultural identity and a strong interest in preserving it.Two decades after those analyses, it is quite clear that this view was mistaken.Instead of successfully mobilizing in defence of their group interests, Ukraine's Russian-speakers have lost much of their distinct ethnocultural identity which should have driven such mobilization.In the face of the Russian aggression of 2014, most Russian-speakers, even in the seemingly pro-Russian east-southern regions, allied with their fellow citizens rather than their linguistic 'brethren' across the border.As the analysis below will demonstrate, their spectacular choice in favour of Ukraine was based on inconspicuous changes in their ethnonational identity over the previous years.Rather than forming into a community distinguished by its main language, they had gradually been transformed from Soviet people into Ukrainians -without drastic changes in their language practice.While most of them remained primarily Russian-speaking, this is not how they would define themselves.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".