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Record W4320150978 · doi:10.55365/1923.x2022.20.72

Estimation of Transport and Functional Convenience of Assessment Areas During the Regulatory Monetary Valuation of Land Plots In Cities

2022· article· en· W4320150978 on OpenAlexvenueno aff
Іryna Kоshkalda, Liudmyla Bezuhla, Olena Trehub, Kseniia Bliumska-Danko, Liudmyla Bondarenko

Bibliographic record

VenueReview of Economics and Finance · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Sustainability and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsValuation (finance)NormativeLand useEconomicsAgricultural landIncentiveNatural resource economicsBusinessMicroeconomicsEcologyFinance

Abstract

fetched live from OpenAlex

Normative monetary valuation of land is used to calculate the amount of land tax, state duty on mines, inheritance and donation of land according to the law, rent for land of state and communal ownership, losses of agricultural and forestry production, as well as developing indicators and mechanisms of economic incentives for the rational use and protection of land.The method of this indicator calculating was changed at the legislative level in 2021, but it is not perfect, as it does not contain a clear algorithm for consideration of all factors, including transport and functional convenience, on land value indicators in cities.There are no recommendations on the selection of evaluation factors, no the list of indicators, any procedure for calculating and interpreting the results and their application in calculating the normative monetary valuation of land, which became the basis for this study.The purpose of the study is to improve the approach to assessing and consideration the factors of transport and functional convenience during the regulatory monetary valuation of land in cities.

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)0.000
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 score0.096
Threshold uncertainty score0.132

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 studies0.0000.000
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 head0.009
GPT teacher head0.194
Teacher spread0.185 · 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 designObservational
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
Published2022
Admission routes1
Has abstractyes

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