The role of extractive industries in developing peripheral Arctic regions of Russia and Canada
Bibliographic record
Abstract
Russian Federation and Canada are the largest arctic powers that have similar features in evolving their Arctic zones. In the mid-1920s both countries formalized their rights to the northern territories. Russian and Canadian arctic regions are located in harsh climatic zones,geographically distant from national political and business centers, poorly populated, and rich in natural resources. At the same time, there are obvious differences in political institutions,“core-periphery” relationships, business organization, and social activities of aboriginal people and newcomers. The purpose of this study is a comparative evaluation how the rich resource base and industrial production impact on the socio-economic development of the Arctic regions of Russia and Canada. To reach the goal authors use the official statistical sources of the Russian Federation and Canada. Case study method, comparative analysis, and econometric calculations are applied. As a result similar and distinctive features of the industrial development of the Arctic regions of these countries were identified. It can be explained, first of all, by the institutional characteristics of Russia and Canada. Comparing an evidence of the leading extractive companies completed the empirical analysis. Authors concluded that the regions under consideration are characterized by a high or medium share of the extractive industry in the regional economy. Specialization in natural resources extraction and primary processing does not have a negative impact on the economic development of the territories. However, outer companies are engaged in this business that increases the dependence of the regional economy on the conjuncture of world markets. The article investigates in empirical studying common features of the extractive industry in the peripheral Russian and Canadian Arctic territories and its impact on the socio-economic development of these regions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".