La face cachée de la Révolution orange : l'Ukraine et le déni de son problème régional
Bibliographic record
Abstract
The Orange Revolution's hidden face: Ukraine and the denial of its regional problem The 2004 Orange Revolution, an unprecedented popular mobilization against the falsification of presidential election results, revealed an important regional cleavage between a Centre-West massively in favor of the challenger, Viktor Yushchenko, and a South-East no less massively supporting the regime's candidate, Viktor Yanukovych. The 2006 parliamentary elections reproduced this geographic polarization. This division is caused by identity rather than economic factors, but only if non-traditional categories are used to examine ethnicity or language. The consolidation over many generations of a bi-ethnic structure in the South-East, the use of language as a symbol of a regional perspective anchored in history, and the aims of elites for inclusion in, rather than separation from, central politics are factors that help us better grasp the regional dimension that the Orange elite tends to deny.
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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 | 0.010 | 0.010 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".