International experience in forecasting socio-economic development: Possibilities of application in Russian practical activities
Bibliographic record
Abstract
Aim. The work aimed to determine, based on the analysis of international experience, the aspects (methods of organizing the system, techniques and tools) relevant for improving the forecasting system of socio-economic development of the Russian Federation (RF). Objectives. The work seeks to analyze the experience of socio-economic forecasting in the USA, Canada, Germany, France, the People’s Republic of China (PRC) and a number of other countries; to identify the features of the North American, Asian and European forecasting systems; to determine the aspects (measures, mechanisms and tools) of international experience, applicable to forecasting the socio-economic development in Russia at the regional level. Methods. A number of methods were used in the study, including deduction and induction, comparative and cause-and-effect analysis, synthesis of a number of sources, including state and regional strategies, regulatory legal acts of the Russian Federation, the European Union (EU), the USA, Canada, Japan, China, and a number of other countries, as well as generalization, specification, and graphical interpretation. Results. An analysis of international experience in socio-economic forecasting systems has revealed key practices that can be applied to improve the efficiency of the Russian forecasting system. These practices include the creation of special bodies (in the public administration system) with tasks of coordination and methodological support of forecasting and planning processes at various levels; systematic modernization and updating of methodological documents on forecasting; inclusion in the forecasting and planning process of events for high-quality and large-scale public discussion of forecast and strategic documents; ensuring the consistency of forecast documents developed for different periods; development of commercial forecasting and cooperation between federal and regional authorities, which are subjects of forecasting socio-economic development, and university innovation and analytical centers. Further research in this field can be aimed at developing recommendations for adapting international experience to Russian conditions.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| 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".