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Record W3208525317 · doi:10.17673/ip.2018.3.09.4

HOW TO REGENERATE THE HISTORICAL ENVIRONMENT OF RUSSIAN CITIES: PARTICIPATION AND REGULATION TOOLS

2018· article· en· W3208525317 on OpenAlexaboutno aff
Snezhana Vladimirovna Lashchenko, Evgenia Aleksandrovna Repina, Boris Grozovsky

Bibliographic record

VenueInnovative Project · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Socio-Economic Development Trends
Canadian institutionsnot available
Fundersnot available
KeywordsCitizen journalismZoningQuarter (Canadian coin)Urban planningArchitectureSettlement (finance)Environmental planningCapital (architecture)Plan (archaeology)Political scienceRegional scienceGeographyCivil engineeringEngineeringArchaeologyBusinessLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.830
Threshold uncertainty score0.232

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.096
GPT teacher head0.327
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2018
Admission routes1
Has abstractyes

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