Territorial distribution of investments in Russian cities in 2015-2018
Bibliographic record
Abstract
This article is devoted to analysis of the territorial investment distribution in Russian Federation cities in depending on population and economics-geographical factors. The main aim is gives raised researchers influence to the regional heterogeneity of investments distribution in cities and investment aspects of big cities development. Herewith, the shortage of works, devoted to investment situation in cities with population less 100 thousand, is remained. The work conclusions are based on the analysis of the investment distributions to the main capital for 1066 Russian Federation cities over the period from 2015 to 2018 years. Based on this analysis, conclusions are made about hard investment territorial distribution dependence from the regional situation. The cities influence to the investment situation firstly manifests in the Moscow and the St. Petersburg agglomerations. But even other cities over a million people mostly depends on its regional economic. It was found, that investment distribution dependence from the city’s population has a nonlinear character. The distortions appear because of small oil-gas cities and the large and largest cities underinvestments. The polarization is especially strong among the small towns: 2% of all the settlement with population less than 50% concentrates almost quarter of investments for this cities group. It was found, that the investment activity for most of the cities doesn’t bring comparable results for cities economics and budget.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".