Urban Regimes in Russia’s Northern Cities: Testing a Concept in a New Environment
Bibliographic record
Abstract
At a time when urbanization represents a major trend in human history and when the majority of the world’s population lives in an urban environment, the urban regime theory, developed by Clarence Stone in the 1980s, offers an insightful framework for discussing how urban stakeholders are compelled to work together to achieve their goals. While research on urban regimes has historically focused mainly on democratic contexts, this article argues that it is time to use urban regime theory in authoritarian or semi-authoritarian countries in order to better understand how urban politics develop. With growing urban activism and huge territorial contrasts, Russia offers a good case study for testing the notion of “urban regime.” This article focuses on three cities in Russia’s Far North—Murmansk, Norilsk, and Yakutsk—that face common sustainability challenges in Arctic or subarctic conditions; it delves into the mechanisms of their urban regimes and categorizes them by type: instrumental, organic, and symbolic.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".