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THE CURRENT STATE OF ASSESSMENT ZONING OF TERRITORIES: RUSSIAN AND FOREIGN EXPERIENCE

2025· article· en· W4413950641 on OpenAlex
Yanina Zaitseva, Y Y Savchenko

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMoscow Economic Journal · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Sustainability and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsZoningCurrent (fluid)State (computer science)Political scienceEnvironmental planningGeographyGeologyComputer scienceLawOceanography

Abstract

fetched live from OpenAlex

The article considers the key role of appraisal zoning in the system of state cadastral valuation and taxation of real estate. The methodology of forming homogeneous appraisal zones as a basis for mass cadastral valuation of real estate is consistently disclosed. The rationale is that appraisal zoning is a methodologically significant tool for increasing the accuracy, objectivity and economic confirmation of cadastral value calculations. The main principles of zoning are set out, and the algorithm for forming zones is described. The functioning of the Russian appraisal system is considered, a comparative analysis of zoning practices in leading foreign countries is given: the USA, Germany and Canada. The article shows that, despite the differences, all approaches use GIS and statistical methods to ensure a fair tax base, and reveals the pros and cons of each system, suggesting possible areas for improving domestic practice in the light of international experience.

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.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.338
Threshold uncertainty score0.258

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.004
GPT teacher head0.251
Teacher spread0.247 · 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