Evaluation of Cultural Impact on Regional Economic Development in Russia
Bibliographic record
Abstract
Paper is devoted to Dependence of economic development of the Russian regions on their cultural level indicators. It determines and analyzes the influence degree of cultural components of the region development on its economic factors. The investigated statistical base consists of selected cultural and economic indices taken in the period of 2000-2015 years. The hypothesis of significant influence degree of the visits to museums and in particular, theatres number, on the amount of implemented innovational technologies was confirmed. A hypothesis about the close relationship between the volumes of expenditures of the Russian budget for culture and the level of research and development work, as well as directly the number of innovative industrial technologies introduced was confirmed. Hypotheses about the close interaction of cultural indices and such macroeconomic parameters as GDP growth, the volume of the capital investments have not been confirmed. A weak correlation between library holdings volume and the studied economic indicators was noted. The research can be used in design of the regional development programs, in forming budget priorities of budgets projects, or in taking other management decisions programming the basis for effective social and economic policy of the regions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".