“That’s how we know they’re healthy”: the inclusion of traditional ecological knowledge in beluga health monitoring in the Inuvialuit Settlement Region
Bibliographic record
Abstract
Belugas (Delphinapterus leucas) from the Eastern Beaufort Sea (EBS) population are harvested annually in the Inuvialuit Settlement Region (ISR) during their seasonal migration past coastal communities and harvest camps. The beluga harvest monitoring program is a flagship program of the ISR’s Fish and Marine Mammal Community Monitoring Program, and it has provided critical information about beluga health and observed changes in the EBS population. This study aimed to develop a suite of local indicators of beluga health that bridged traditional ecological knowledge (TEK) about beluga condition, illness, and disease, with western science through the co-production of knowledge. Community members from Inuvik, Paulatuk, and Tuktoyaktuk with beluga harvesting and preparation experience were engaged to characterize beluga health from an Inuvialuit perspective. Inuvialuit knowledge about the environment and beluga health, values about hunting beluga, and Inuvialuit cosmology — the foundation of the knowledge system — were documented through semi-structured questionnaires (n = 66), semi-structured interviews (n = 78), and focus group meetings (n = 3). This research furthers our understanding of how Inuvialuit view beluga health from the physical and behavioural characteristics of belugas, values, and appropriate behaviours by harvesters and how observations made about beluga can be explained. To support the co-production of knowledge, a suite of local indicators was developed that bridged TEK about beluga condition, illness, and disease with western science.
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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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".