Geographies of Toxic Persistence: Environmental Degradation in Russia's Industrial Regions
Bibliographic record
Abstract
Abstract Despite growing global concern about the environment; growing numbers of international agreements designed to mitigate the effects of pollution; and numerous governmental regulations adopted to improve environmental conditions in the Russian industrial regions of the North (Norilsk), and the large-scale industrial region of the Urals, environmental damage has continued to increase. The long-standing issue of environmental damage in Russia’s industrial regions is the focus of this research question: what are the geographic factors that have allowed environmental degradation to persist in these areas? Utilizing an extensive mixed-methodology study of both geospatial analysis and historical reviews of industrialization under the Soviet Union, along with in-depth qualitative case studies of Norilsk and the Urals, this study identifies and analyzes the key drivers that allow environmental degradation to continue to be a problem in Russia’s industrial regions. These results show how the specific geographic characteristics of each of these places have synergized historically with the legacy of the Soviet Union's industrialization policies, which emphasized the importance of production over the environment, and economically today, with the imperative to extract resources for the financial benefit of Russia. This study has significant implications for the development of environmental policies in other areas of the world where natural resources are being extracted at high volumes, such as Canada's oil sands and Australia's mining frontier, promoting the need for geographically-tailored strategies, i.e., increased use of remote sensing technologies and decentralized governance structures. Additionally, this study builds upon current scholarship in the field of environmental geography, specifically in post-industrial and post-Soviet contexts, to better understand the role of physical and human geographies in sustaining ecological crises, while also addressing a gap in the literature in terms of understanding the relationship between time and space in analyzing the persistence of ecological crises.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".