Negotiated Agreements and Sámi Reindeer Herding in Sweden: Evaluating Outcomes
Bibliographic record
Abstract
In the European north, there is a growing trend for Smi reindeer herding communities to enter negotiated agreements with developers on projects that aim to exploit land and natural resources.This paper offers, for the first time, an evaluation of the content of a selection of these agreements, drawing on a sample of 15 agreements from five communities in Sweden.The evaluation was conducted from a Smi perspective on how the agreements affect the ability of herding communities to safeguard reindeer wellbeing.The overall conclusion is that the agreements provide some positive contributions to mitigate harm to the reindeer, but also contribute considerable risks.We argue that agreements might have a meaningful role to play in the integration of Smi rights in land and resource decisions, but herding communities have considerable space to increase the range of clauses -guided by larger goals of Smi self-determination.
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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 | 0.001 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".