Social and Environmental Impacts of Development on Rural Traditional Arctic Communities: Focus on Northern Sweden and the Sami
Bibliographic record
Abstract
In the past few decades, the Arctic has become of keen interest for nations and developers around the world.This can be seen in the development of industries, establishment of Arctic centric organizations, as well as the expansion of the Arctic Council's membership.Countries with at least some part located above the Arctic Circle are; Norway, Greenland, Canada, United States, Russia, Finland, and Sweden.The focus on the indigenous people, specifically the Sami of Lapland, presents a good case for assessing impacts of development on northern, Arctic, communities.In Sweden, the population density is recorded as 21.4 people per square kilometer, with a higher population density in southern Sweden.The majority of the Sami people live in small to medium-sized communities, in remote regions, often resulting in a disconnect with the modern world.One industry that is already present in the Swedish Arctic is mining, especially for ore and carbon.However, now other industries, including multinational enterprises (MNE's), such as Facebook, which recently built a new five acre data center near the Arctic Circle, are beginning to realize the opportunities the Arctic region and its environment have to offer.Many are asking how sustainable is business development in the Arctic which can be answered by analyzing the impacts on the Sami communities and how people react and should react to these changes within their communities.This study analyzed current events through literature review and interviews of representatives from the impacted Arctic regions.The increased development has resulted in both negative and positive impacts such as reduction of land use, but increased employment opportunities.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".