Review and mapping of Indigenous knowledge concepts in the Arctic
Bibliographic record
Abstract
The importance of using knowledge of Indigenous peoples alongside science in research, management and resource development is increasingly acknowledged. Despite political intentions of including the knowledge of Indigenous peoples, the extent and quality of utilizing their knowledge is uneven in the Arctic. The lack of agreed-upon definitions of various concepts used for the knowledge of Indigenous peoples, and their interchangeable and inconsistent use, creates confusion about their meaning and implications. In this chapter, we review the knowledge concepts and their interrelatedness, developing concept maps to visualize their similarities and differences with a view to clarifying the confusion and aiding a more consistent engagement and utilization of this knowledge. We argue that Indigenous knowledge is the only concept that emphasizes the identity aspect and thus implies the distinct status and collective rights of Indigenous peoples, distinguishing it from other knowledge concepts. Our review suggests that the use of concepts varies significantly in the Arctic, shaped by the colonial and political-economic processes in Greenland, the Canadian Arctic and Alaska. We also observe a transition in use of concepts from traditional knowledge to Indigenous knowledge.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.025 | 0.030 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".