Municipal Programs and Sustainable Development in Russian Northern Cities: Case Studies of Murmansk and Magadan
Bibliographic record
Abstract
Cities play an important role in promoting sustainable development. In the Arctic, most particularly in Russia, cities concentrate the majority of residents and economic activity. Sustainable development initiatives are often deployed through programs that operate at different spatial and jurisdictional scales. While national and regional policies and programs have received some attention, the understanding of urban development policies and programs at the municipal level in the Arctic is still limited. This paper presents a case study of municipal sustainable development programming in Arctic cities and examines municipal programs in two larger Russian northern cities: Murmansk and Magadan. While both are regional capitals and the most populous urban settlements in their regions, the cities have district historical, economic and geographical contexts. Through the content analysis of municipal programs active in 2018, we aim to understand, systematize and compare the visions and programmatic actions of the two municipalities on sustainable development. Ten sustainable development programming categories were identified for using a UN SDG-inspired approach modeled after the City of Whitehorse, Canada. While the programs in Magadan and Murmansk are quite different, we observed striking commonalities that characterize the national, regional and local models of urban sustainable development policy making in the Russian Arctic.
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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 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".