Estimating the Economic Value of Narwhal and Beluga Hunts in Hudson Bay, Nunavut
Bibliographic record
Abstract
ABSTRACT. Hunting of narwhal (Monodon monoceros) and beluga (Delphinapterus leucas) in Hudson Bay is an important activity, providing food and income in northern communities, yet few studies detail the economic aspects of these hunts. We outline the uses of narwhal and beluga and estimate the revenues, costs, and economic use value associated with the hunt on the basis of the harvests in 2007. We also explore the effects of cost sharing and inclusion of opportunity cost of labour on model outputs. For the communities participating in each hunt, the average economic use value was negative (-$9399) for beluga and positive ($133 278) for narwhal. The corresponding per capita value estimates were-$1 for beluga and $44 for narwhal. Including the effects of cost sharing with one other hunting activity in the model increased the economic use values to $266 504 for beluga and $321 500 for narwhal. Narwhals provide a higher value per whale, in addition to a higher per capita total economic value to the community, compared to belugas because resources are shared among fewer communities. However, the beluga hunt overall provides greater revenue because more belugas are harvested. In keeping with literature on other hunting activities in the Arctic, our results indicate that the value of whales to communities is largely due to their food value. Key words: hunting, narwhal, beluga, economic value, Hudson Bay, subsistence hunting, use value RÉSUMÉ. Dans la baie d’Hudson, la chasse au narval (Monodon monoceros) et au béluga (Delphinapterus leucas) représente une activité importante en ce sens qu’elle est à la fois une source de nourriture et de revenu pour les collectivités du Nord. Pourtant, peu d’études se penchent sur les aspects économiques de cette activité. Nous faisons mention des utilités du narval
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".