SMALL INDUSTRY ENTERPRISES SUPPORT IN THE ARCTIC: RUSSIAN AND FOREIGN EXPERIENCE
Bibliographic record
Abstract
The Russian Arctic is an industrially developed macro-region with a number of socio-economic features, which include low population density, high resource intensity of economic activity, low level of development of transport, logistics and social infrastructure, raw materials orientation of the economy. Analyzing the existing small forms of industry on the territory of the Russian Arctic, the authors divided them into three main categories: enterprises engaged in servicing the needs of large industrial facilities; industrial enterprises of local importance and enterprises producing products for export. The article considered the Russian and foreign practice small and medium-sized enterprises (SMEs) support in the field of industry in the Arctic territories. In Russia, the main measures to support small forms of industry are: the provision of preferential loans and loans; the provision of land plots, premises and other property owned by the state; consulting services for doing business. It is noted that the following support measures applied in Finland and Canada can be successfully adapted to the Russian practice of state support of small industrial enterprises: building interregional forms of cooperation (coordination and cooperation); support of enterprises on the sectoral principle; support of entrepreneurs, including representatives of the native people of the North.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".