Comparative Analysis of Regional Development of Northern Territories
Bibliographic record
Abstract
The analysis of northern regional development on the basis of the synthesis of comparative research andhistorical method is carried out in the article. The study of social and economic system enables us to understandbetter the roles of factors and methods of the governmental control of regional development as well as theirposition in social and economic and geopolitical aspect.The northern regions of such countries as Russia (Kamchatka Krai), Canada (Yukon), the USA (Alaska), Japan(Hokkaido), and Iceland and Greenland were chosen as an object of research. All these regions play astrategically significant role in social and economic development of own countries and have climatic similarities.The determination of common features of regional social and economic modeling makes it possible to comparethe development of regions, having the same climatic, economic and geopolitical conditions but different levelsof economic activityThere is the statistics generalization, characterizing various activities of regions, including structure of economy,its infrastructure supply, demographical situation, and financial sphere, that enables to see the features ofnational models of regional economies development. It is also proved that ignoring the actual connectionbetween regional system elements and external and internal factors leads to loosing historical and social andeconomic basis in research.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".