Preferential Regime of the Russian Arctic: Tendencies and First Results from Realization of the World’s Largest Special Economic Zone
Bibliographic record
Abstract
The preferential regime of the Arctic Zone of the Russian Federation is the latest regulatory mechanism designed to overcome negative socio-economic trends in the macroregion. The accumulated factual data over the three-year period of this work have made it possible to make the first reasonable estimates of its effects on the regional economy. The purpose of this study is to investigate the presence of transformational changes in the relationship between employment and investment due to the introduction of the preferential regime for key sectors in the regions that are fully or partially included in the Russian Arctic. The relationship between investment and employment in regional industries was studied using least squares regression analysis using Advanced Grapher 2.2 software. The results show, firstly, significant differences in trends in the implementation of preferential treatment: increased economic specialization of some regions and diversification of the economies of other regions. Secondly, there is a slowdown in the emergence of new projects. Thirdly, the markedly different employment effects across industries and regions of the Russian Arctic, as well as the changing nature of the relationship between investment and employment, require a significant revision of regulatory measures and economic policies to maximize regime effects and achieve sustainable long-term regional development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".