Resonance Strategies of Sámi Reindeer Herders in Northernmost Finland during Climatically Extreme Years
Bibliographic record
Abstract
This study focuses on the resonance strategies of Sámi reindeer herders in four reindeer-herding cooperatives in northernmost Finland in climatically extreme years, specifically those occurring during the period 1970–2007. “Resonance” is an instinctive and indwelling reaction of a herder to a specific change (in contrast to coping, which is a more general response). The study is based on interviews with herders, field experiences, reindeer population statistics, and weather data. Before the 1960s, herders were able to deal with changing weather conditions by using intensive herding techniques and semi-tame reindeer. After the 1960s, reindeer became wilder because of the use of snowmobiles and more extensive herding techniques. The herders of the fell and forest cooperatives did not have sufficient means to prevent the serious reindeer losses in 1972–74, which resulted from two years of hard snow and ice cover, hot summers, and the free ranging of loose herds. In each of the four cooperatives studied, most of the old siida herds were combined, and one solution to handling large, loose herds was to build fences between cooperatives. Since the 1990s, all four cooperatives have used diverse herding and pasture rotation strategies to cope with the critical winter months. The herding techniques and the human-reindeer relationship in the fell cooperatives have differed from those in the forest cooperatives mainly because of differences in pasture types, topography, and microclimate. The contrast can be seen particularly in snow and ice conditions, as open fell regions have a thin and compact snow cover, whereas forest regions typically have deep, soft snow. This research shows that the resonance strategies of Sámi reindeer herders are both heterogeneous and dynamic: herders change them constantly, drawing on both old and new techniques to deal with the variable weather.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".