When research relies on wildlife samples obtained from communities: a case study on local cultural contexts of beluga whale (<i>Delphinapterus leucas</i>) harvesting in Tuktoyaktuk, NT and Arviat, NU
Bibliographic record
Abstract
Each community across Inuit Nunaat has specific histories, geography, and cultural norms or practices when it comes to beluga ( Delphinapterus leucas) harvesting. These coastal communities across Inuit Nunaat range over vast distances but share some similarities. Wildlife samples obtained from Inuit harvesters provide much of the data for current scientific literature in the Arctic. We use beluga as a case study to demonstrate the importance and value of including local contexts in wildlife research by focusing on Arviat, NU and Tuktoyaktuk, NT and their local cultural contexts of beluga harvesting and sampling. Local harvesters were interviewed about beluga to characterize the beluga hunting season, beluga harvest preference and selection, which cuts are preferred, how to examine beluga health, and research priorities. There were marked differences in all these areas between communities, except for how harvesters assess beluga health, which were similar. We also highlight potential research directions raised during the interviews. These findings confirm that there are cultural differences in beluga harvesting between these two communities. These harvest preferences should be accounted for in scientific interpretations of the data, which are often entirely derived from hunter-harvested animals. It follows then that local cultural preferences can result in biases for certain animals (size, colour), as we have shown, illustrating the importance of considering local contexts when conducting wildlife research.
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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 | 0.036 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.025 | 0.017 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| 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".