Changing Winter Landscapes: Extreme Weather Events and Meanings of Snow for Sámi Reindeer Herders
Bibliographic record
Abstract
Snow is a crucial part in the lives of Sámi reindeer herders, and changes in snow conditions can affect their well-being in multiple ways. However, meanings and emotions associated with snow are rarely considered in research on reindeer herding and climate change. Based on thematic interviews with reindeer herders in two reindeer herding co-operatives in the Sámi Homeland in Finland, we examined the roles and meanings of snow for Sámi reindeer herders and impacts of the extreme winter events of recent years on their well-being. In addition, based on a literature survey, we considered the role of reindeer herders’ snow knowledge in climate change research related to the Sámi area in Finland, Sweden, and Norway. Our results show that snow plays multiple roles in the lives of reindeer herders. The extreme snow conditions of recent years have had a significant negative impact on reindeer herder well-being, and at the same time, snow is connected to happiness, sense of place, and cultural continuity. The embeddedness of snow with different kinds of cultural and intrinsic meanings should receive more attention in research on the impacts of climate change on the lives of Sámi and other Arctic peoples. In the literature we analyzed, the snow knowledge of Sámi reindeer herders was constructed in multiple ways. This practical knowledge system informing, as it does, daily activities and assessments of the future, is not only crucial for reindeer herders themselves, but also for society at large, as it can enhance education and bring important insights into climate change research and adaptation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".