Incorporating Traditional Ecological Knowledge into Western science in the Arctic Council: Lip service?
Bibliographic record
Abstract
The utilization of Traditional Ecological Knowledge (TEK) in wildlife management has been a prominent topic for several decades. Since its establishment, the Arctic Council (AC) has emphasized the importance of TEK and its utilization in its work. Yet, the AC has not been successful in the process of knowledge coproduction. Why has TEK not been meaningfully incorporated into the Arctic Council? To answer this question, the study created and applied the Participation-Indigenous-Local-Application-Cross-cultural evaluation scale to the AC documents in order to analyze to what degree TEK has been incorporated into them. The research included interviews with 15 Indigenous leaders, officials, and scholars who were involved in the work of AC and/or worked with Indigenous communities and TEK projects. This study argues that lip service occurred as a result of several factors: state diversity in the perception of TEK as a concept, lesser effectiveness of Permanent Participants in the incorporation of TEK, politicization of TEK, and the resistance of Western scholars to TEK.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| 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".