Parallel Patterns of Power? Local Government Reform in Late Imperial and Post Soviet Russia
Bibliographic record
Abstract
Throughout the reign of President Boris Yeltsin, the vast majority of scholarly attention from the West concentrated on national politics in Russia - the political and economic developments in and around Moscow. Thus, federal institutions, federal elections, federal executive-legislative relations, national political parties and national personalities, as well as national fiscal and economic policies were the main objects of study. Even in the study of Russia's disparate regions, the bulk of attention was most often directed towards how federal relations between, for example, Sverdlovsk and Moscow affected the federal government rather than Russian society as a whole. Of course, Russian transitology requires a sharp focus on national issues. Building even a semi stable democracy would require Russia to make significant progress in all issues noted above. Yet too often overlooked in all this academic output has been what is happening in the everyday lives of common people. Continual reports of wage arrears and declining living standards were shunted aside to focus on corporate governance and regression analysis. The pursuit of statistics to bolster the claim that Russia had crossed the valley of transition overshadowed the poverty and misery of everyday life. As in Russia's past, the means of reform took a back seat to the intended ends, as formulated and pursued by thinkers and theorists. Thus, for example, privatization was considered successful because 70% of enterprises fell under private control. But the very process of privatization, and the consequences of non production from racketeers with title to these same enterprises should have sent alarm bells ringing many years earlier. It is little wonder that after years of looking the wrong way, too many Western observers are now surprised that Russian politics and society seem so upside down. This study is based on two propositions. First, while scholars have been encouraged to utilise comparative analysis in the study of post Soviet Russia, it is important to note that the building and testing of theory may come at the expense of understanding the object of study. This is especially evident when scholars are more wedded to methodology and theoretical claims than to empirical evidence.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".