Development of Tools for Synergy of Social Functions of the State and Housing Mortgage Loans in Russia: Regional Analysis of the Central, Southern and Volga Federal Districts
Bibliographic record
Abstract
The article researches the features of the synergy of the social functions of the state and the housing mortgage loan (HML) in order to develop a tool that allows determining guidelines and directions for strengthening the effectiveness of collaboration between the state and the private sector represented by commercial banks in solving the most important social problem—providing housing to the population. The authors show that the use of the proposed assessment tool by state structures and commercial banks increases the effectiveness of solutions to the housing problem in the country and enhances the synergetic effect of a comprehensive increase in the standard of living of the population when synchronizing actions. The main purpose of the research was to develop an algorithm that determines the key factors influencing the number of issued HML. The object of the study is the Russian HML market on the example of three federal districts. The developed algorithm is based on the use of statistical analysis methods ANOVA, mutual regression and recursive feature elimination. The approbation of the results obtained on three subjects of the Russian Federation allowed us to obtain a set of significant factors of influence, taking into account regional peculiarities.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".