HOW TO REGENERATE THE HISTORICAL ENVIRONMENT OF RUSSIAN CITIES: PARTICIPATION AND REGULATION TOOLS
Bibliographic record
Abstract
The historical environment of Russian cities is in decline. Buildings are regularly demolished; the territory of old centers is reduced. Neither experts nor the authorities have an idea of how to preserve and develop the historical parts of cities. Meanwhile, foreign and partly Russian practice allows us to highlight the tools for the regeneration of the historical environment. They were discussed at the 9th International Forum Urban Growth and Preservation of Heritage along the Eurasian Corridor (Silk Road) in Samara (October 2017) by Russian experts in the field of architecture, urban planning and urban studies. These are, firstly, instruments of participation (participatory design and participatory budgeting), and secondly, regulatory instruments (master plan, zoning, an instrument that protects groups of environmental objects, and a historical settlement that allows you to comprehensively regulate the entire historical center with its environmental objects, not just monuments). The article also considers the impact of social capital on the development of the urban environment and discusses which principles of the historical quarter can be applied in modern design.
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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 | 0.012 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".