Beluga hunters in a mixed economy: managing the impacts of nature-based tourism in the Canadian western Arctic
Bibliographic record
Abstract
Abstract The Inuvialuit Region of the Canadian western Arctic continues to support a variety of land-based activities as part of the regional mixed economy. Tourism development, one of the newer elements of the mixed economy, has potential to conflict with beluga whale hunting, one of the traditional activities. The paper asks the question: can local employment be created through nature-based tourism development in Inuvik, Aklavik, and Tuktoyaktuk in the Inuvialuit Region in ways that support the local mixed economy and minimize conflict with the traditional sector? Results of interviews with Inuvialuit elders and tour operators indicate that both parties regard tourism as a desirable employment option and a creator of economic benefits, with relatively few economic drawbacks and relatively little environmental concern. The problem, however, is that tourism also brings with it social impacts and cultural drawbacks that are, in the Inuvialuit view, mostly related to (a) intrusiveness of tourists, especially in relation to the beluga hunt; (b) representation of the aboriginal hunt in a negative light; and (c) commodification of culture. On the balance, nature-based tourism development has the capability to support the local mixed economy, subject to resolving the conflict between beluga whaling activities and tourists. Fundamentally, however, the conflict is between Inuvialuit lifestyles and values versus the values and expectations of tourists.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".