Population dynamics of arctic cities in Russia and Canada since the mid-20th century
Bibliographic record
Abstract
The paper addresses the problem of universalizing the characteristics of Arctic cities and questions the possibility of identifying uniform patterns in their demographic dynamics. The analysis is based on a comparison of census data for more than 100 cities in Russia and Canada since the mid-20th century. The authors demonstrate that the official definition of the Arctic Zone as an administrative category does not reflect the real differences between cities. The authors show that the official definition of the Arctic zone as a management category does not reflect the real differences between cities because there are radically different development trajectories within the boundaries of both the modern Arctic zone of the Russian Federation and the North of Canada. One of the trajectories is analyzed in detail using the example of the town of Norilsk (the modern Central District of the town of Norilsk). This is a frontier type of population dynamics: the decrease in population numbers is caused not so much by the outflow of population as by a decline in incoming migration while an overall high migration turnover is constant. The findings emphasize the need to move away from standardized planning scenarios and to consider the diversity of local conditions in managing the development of Arctic territories.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".