How Traditional Knowledge Comes to Matter in Atlantic Salmon Governance in Norway and Finland
Bibliographic record
Abstract
The article compares different models for knowledge production, all of which include traditional knowledge, as part of Norwegian and Finnish Atlantic salmon (Salmo salar) research and management projects. Our hope is to contribute constructively to more socially robust knowledge production in Arctic environmental governance. Through investigating how traditional knowledge comes to matter at local, regional (national), and international levels in different Atlantic salmon research and governance projects in Norway and Finland, we examine the social robustness of different approaches to knowledge co-production. In general, the projects that seem to fulfill Arctic expectations of traditional knowledge co-production with science (projects with high legitimacy) seem to have the least impact on policy, and vice versa. We argue that expectations at the international policy level towards traditional knowledge integration with science are at times unrealistically high and hard to meet at local levels and in national policy contexts. We therefore argue for rethinking how a legitimate and policy-relevant knowledge co-production process should be conducted. Arctic policy levels, Norwegian and Finnish environmental authorities, and salmon conservation science could fruitfully draw lessons from the Näätämö co-management project, which is already referred to as an example of best practice in Arctic environmental governance. To achieve social robustness, projects need to balance scientific credibility with legitimacy among local and Indigenous rights holders. This balance might entail giving up on expectations of integrating traditional ecological knowledge with science and embracing the undefined spaces within Arctic and Indigenous knowledge production.
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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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".