Managing predators, managing reindeer: contested conceptions of predator policies in Finland's southeast reindeer herding area
Bibliographic record
Abstract
ABSTRACT Preserving biodiversity and establishing healthy and thriving populations of predator animals are the expressed aims of many wildlife and ecosystem conservation projects and initiatives. However, such conservation strategies are often in conflict with the traditions, practices and land-use priorities of local communities. This article concentrates on the situation concerning the predation of reindeer (mainly by wolves) in Finland's southeast reindeer herding area and its immediate vicinity, but makes reference to the broader situation of predation and reindeer herding in Finland. Based on analysis of statistics and interviews with local stakeholders, the research findings refer to the intermingled contradictions related to conceptual, statistical and other management relevant knowledge and resulting problems, for example, in conservation hunting licensing. The article concludes that the wolf comprises a complex case for nature conservation initiatives and sustainable reindeer husbandry and that, in practice, it has particular implications compared to other policy approaches to dealing with the problem of animal predators. The article ends with some theoretical considerations as to whether we can improve our understanding of modern human-environment relations by deriving ideas from the actor-network theory debates.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".