Socio-Economic Potential of the Region and Its Evaluation
Bibliographic record
Abstract
At the moment the problem of determining the internal reserves of socio-economic development of the regionbecomes more important, the solution of which requires the development of new approaches to the definition ofthe essence, structure, methods for assessing the socio-economic potential.Modern socio-economic situation and the need for constant adjustment of the processes occurring in the regionrequire the development and formation of the conceptual and methodological tools of complex analysis of thelevel of development potential of the area. Now there are several basic approaches: integral evaluation ofmeasures of socio-economic prosperity of the regions; integrated comprehensive performance evaluation basedon the additive and multiplicative criterion.One of the policy objectives of socio-economic development is the establishment of long-term regionaldevelopment priorities. Comprehensive assessment of the dynamics of development of the Russian Federationsuggests some stabilization of the socio-economic situation in them. However, some regions are significantlybehind in terms of its socio-economic development. Therefore strategically important for Russia is a coherentstate regional policy.The aim of this study is the development of theoretical approaches, methodological principles, as well as thedevelopment of practical recommendations for a comprehensive assessment of the socio-economic potential ofthe region.During the study of the theoretical framework for assessing the socio-economic potential of the technique in thebalance of social and economic trends on the basis of the calculation of integral indices reflecting the substantialcharacteristics of the local units were constructed indicators (normalized values) underlying the integralevaluation of the balanced socio-economic potential of the region. The application of this approach allows toselect the subjects of the Russian Federation, whose rating is the same when using the resource and effectiveapproaches that demonstrates the effectiveness of management of socio-economic potential of territorial entities.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".