“DE-FEDERALISATION” VS. DEMOCRATISATION The Politicisation of Federal Reforms in Russia
Bibliographic record
Abstract
Which type of federalism can best be combined with what form of democratic system remains a puzzle in both democratisation and federalism literature. The Russian case illustrates in multiple ways that federalism does not work if democratic structures on the national and regional level are dysfunctional. The aim of this article is two-fold: first, it aims to delineate how federal reforms in Russia during the last decade have led, through politicisation, to a process of de-federalisation rather than to the construction of a strong federal system in the country. Second, by means of analysing selected federal reforms, the question will be explored whether the process of de-federalisation has correlated with, if not contributed to, the dismantling of democratic elements in the country. In sum, this article seeks to make a case for the continued combination of federalism and democracy studies in order to advance theory-building in the application to specific regional contexts.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".