The need for transformative changes in the use of Indigenous knowledge along with science for environmental decision‐making in the Arctic
Bibliographic record
Abstract
Abstract Recent attention to the role of Indigenous knowledge (IK) in environmental monitoring, research and decision‐making is likely to attract new people to this field of work. Advancing the bringing together of IK and science in a way that is desirable to IK holders can lead to successful and inclusive research and decision‐making. We used the Delphi technique with 18 expert participants who were IK holders or working closely with IK from across the Arctic to examine the drivers of progress and limitations to the use of IK along with science to inform decision‐making related to wildlife, reindeer herding and the environment. We also used this technique to identify participants' experiences of scientists' misconceptions concerning IK. Participants had a strong focus on transformative change relating to the structure of institutions, politics, rights, involvement, power and agency over technical issues advancing or limiting progress (e.g. new technologies and language barriers). Participants identified two modes of desirable research: coproducing knowledge with scientists and autonomous Indigenous‐led research. They highlighted the need for more collaborative and coproduction projects to allow further refinement of approaches and more funding to support autonomous, Indigenous‐led research. Most misconceptions held by scientists concerning IK that were identified by participants related to the spatial, temporal and conceptual scope of IK, and the perceived need to validate IK using Western science. Our research highlights some of the issues that need to be addressed by all participants in research and decision‐making involving IK and science. While exact approaches will need to be tailored to specific social‐ecological contexts, consideration of these broader concerns revealed by our analysis are likely to be central to effective partnerships. A free Plain Language Summary can be found within the Supporting Information of this article.
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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 | 0.072 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.027 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| 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".