SME Exports in Novosibirsk Region: Sustainable Development Trends
Bibliographic record
Abstract
The study aims at identifying stable trends in the development of export flows by small and medium enterprises at the regional level as exemplified by the Novosibirsk Region (Russia).Studying the sustainability of trends in SME exports at the regional level provides valuable insights into the potential for regional economic growth, diversification, and environmental sustainability, and inform policy decisions to support these goals.Standard statistical analysis methods to reveal trends in export flows in absolute and relative terms, determine the relationship between them and identify their specifics were used.The authors assessed the stability of trends and the volatility of exports by regional small and medium enterprises through the method of dynamic norms.According to the study results, more than half of exports by small and medium enterprises in the Novosibirsk Region are formed by nonresource goods.However, its share in the overall Russian export structure is extremely insignificant, especially considering the region's developed manufacturing sector.This study's methodology and insights offer a significant contribution to the field of regional economic development, making it a valuable resource for policymakers and researchers alike.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".