The role of Indigenous knowledge in environmental health risk management in Yukon, Canada
Bibliographic record
Abstract
OBJECTIVES: This project aimed to gain better understandings of northern Indigenous risk perception related to food safety and to identify the role that Indigenous knowledge (IK) plays in risk management processes to support more effective and culturally relevant benefit-risk (B-R) management strategies. STUDY DESIGN: The project used an exploratory qualitative case study design to investigate the role and place of IK in the management of environmental contaminants exposure via consumption of traditional foods in Yukon First Nations (YFNs). METHODS: Forty-one semi-directive interviews with Traditional Food Knowledge Holders and Health and Environment Decision-makers were conducted. A review and analysis of organizational documents related to past risk management events for the issue was conducted. Thematic content analysis was used to analyze transcripts and documents for key themes related to the research question. RESULTS: There was a recognized need by all participants for better collaboration between scientists and YFN communities. YFNs have been involved in identifying and defining community concerns about past risk issues, setting a local context, and participating in communications strategies. Interviewees stressed the need to commit adequate time for building relationships, physically being in the community, and facilitating open communication. Conducting community-based projects was identified as critical for collaboration and for cooperative learning and management of these issues. CONCLUSIONS: The perception of "effective" benefit-risk management is significantly influenced by the efforts made to include local communities in the process. A set of common guiding principles within a process that brings together people and knowledge systems may provide a more effective way forward in cross-cultural, multiple knowledge system contexts for complex benefit-risk issues than a prescriptive rigid framework.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".