Socio-Economic Systems’ Competitiveness Assessment Method
Bibliographic record
Abstract
Globalization of modern economics forms new economic challenges in order to improve Russian regions’ competitiveness. The regions’ competitiveness significance grows substantially under conditions of the regions’ historically formed economies’ focus; current nature resources use potential and the advantages of the regions’ geographic location for external economic cooperation. Considering these facts, current research suggests a new method of assessing the socio-economic systems’ competitiveness. The authors suggest using the socio-economic system’s competitiveness integral index as the basic competitiveness assessment means. This integral index comprises 4 indicators, defining the system’s functionality, system, proactiveness, and organicity. It is suggested to form private competitiveness indices in long-term and short-term periods in order to assess the system’s competitiveness dynamically. The private competitiveness index in short-term period comprises indicators, defining the functionality and system levels, and the private competitiveness index in long-term period comprises defining the proactiveness and organicity levels. Several economic magnitudes, interpreting the functionality, system, proactiveness, and organicity indicators are presumed for interpreting each of them. A broadened spectrum of economic magnitudes, used for interpreting the assessment indicators, facilitates the involvement of various statistic and empiric data.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.004 |
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".