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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".