Strategies of the Sámi movement in Sweden: mobilization around grievances related to the ecological conditions of reindeer pastoralism, 2012–2022
Bibliographic record
Abstract
Reindeer pastoralism, practiced by groups of the Indigenous Sámi people in Sweden, is being threatened by a new wave of encroachments. In this paper I take stock of how the Sámi movement has mobilized around grievances related to the ecological conditions that support natural pasture-based reindeer pastoralism. I apply the contentious politics approach to social movement theory, and Felix Kolb’s conceptualization of five strategies that social movements have used when interacting with the state to achieve political change. Drawing upon 10 years of data from the Sámi public news service, my study makes three main contributions. First, I identify a set of themes found in the public grievances connected to ecological conditions articulated by reindeer pastoralist organizations in the public sphere that are a focus for mobilization. Second, I present an overview of the five strategies that the Sámi movement applies in claims-making to address those grievances: the public preference mechanism, the political access mechanism, the judicial mechanism, the international politics mechanism, and the disruption mechanism, and show how they relate to one another. Third, I discuss the limitations of current mobilization efforts, and argue that cross-movement coalitions are needed to challenge the hegemonic bloc.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.013 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".