Bibliographic record
Abstract
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".