The mining resource cycle and settlement demography in Malå, Northern Sweden
Bibliographic record
Abstract
Abstract Research on the demographic impacts of mining in sparsely populated areas has focused primarily on relatively large towns. Less attention has been paid to smaller villages, which may experience different impacts because of their highly concentrated economies and their small populations, making them more vulnerable to demographic “boom and bust” effects. This paper examines demographic change in four small villages in northern Sweden, which are located close to several mining projects but have evolved through different degrees of integration with or separation from mining. Using a longitudinal “resource cycle” perspective, the demographic trajectories of the villages are compared to understand how different types of settlement and engagement with mining have led to different demographic outcomes in the long term. While the four villages experienced similar trajectories in terms of overall population growth and decline, their experiences in relation to more nuanced indicators, including age and gender distributions and population mobilities, were different, and potential reasons for this are discussed. Due to data limitations, however, the long-term demographic consequences of mining for local Sami people remain unclear. The paper problematises this research gap in light of general concerns about mining impacts on traditional Sami livelihoods.
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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 | 0.001 | 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) | 0.002 | 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".