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International experience in forecasting socio-economic development: Possibilities of application in Russian practical activities

2025· article· en· W4413169199 on OpenAlexaboutno aff
E. V. Gokhshtand

Bibliographic record

VenueEconomics and Management · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicEconomic and Technological Developments in Russia
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsRegional scienceManagement scienceSociology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.634
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.034
GPT teacher head0.318
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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