MétaCan
Menu
Back to cohort
Record W4214847589 · doi:10.33920/vne-04-2203-02

Financing of development projects in Russia and abroad

2022· article· en· W4214847589 on OpenAlexaboutno aff
R. S. Gubanov

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEconomic and Technological Systems Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsFinanceReal estateBusinessEquity (law)Real estate developmentContext (archaeology)ChinaPortfolioPolitical science

Abstract

fetched live from OpenAlex

The article examines the essence and content of the category "development project". A comparative characteristic of the views of scientists on the definition of the term "development project" is given and the author’s defi nition of the studied category is derived. In the context of expanding economic relations between the state, developers, market agents, investors and development companies, the supply on the real estate market is expanding, the requirements for the formation of a portfolio of investments directed to the creation and implementation of a development project are changing. The author examines the stages of financing development projects, taking into account the need to develop and monitor master plans for the development of the urban economy. The article emphasizes that the central place in the financing of development projects in Russia today is occupied by the account-ESKROU agreement, which is a tripartite agreement concluded between an individual (legal entity), a bank, a developer for the purpose of equity financing of the construction of a residential property. The needs of the company in attracting investments in a development project have been studied on the example of renewing the territories of St. Petersburg. An assessment of the best foreign practice of financing development projects for attracting syndicated loans and ESCROU accounts to the system of financing innovative development programs in China, Canada, Great Britain, and the United States is given.

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 score0.295
Threshold uncertainty score0.391

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 head0.014
GPT teacher head0.194
Teacher spread0.180 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same topicEconomic and Technological Systems AnalysisFrench-language works237,207