Characterizing tourism benefits associated with top‐predator conservation in coastal British Columbia
Bibliographic record
Abstract
Abstract Facing public concern over costs related to top‐predator reintroductions and conservation, ecosystem services such as ecotourism are often used to evoke benefits that outweigh or offset those costs. Quantifying these benefits using rigorous scientific methods can provide confidence to policymakers and other stakeholders that predators can in fact deliver positive outcomes to people living alongside them. The evaluation of these benefits is often anecdotal or qualitative, however, and empirical quantifications are rare. In coastal marine ecosystems, sea otter reintroduction is seen as a conservation success to some but a bane to others. The contribution of sea otters (Enyhdra lutris) to tourism revenue is touted as a crucial ecosystem service benefit to offset the loss of shellfish harvesting and associated revenue, but remains unquantified, weakening the favourable reception of conservation action. The potential economic benefits of sea otters associated with tourism and the extent to which benefits are realized were evaluated based on: (i) choice‐experiment surveys of tourists; and (ii) interviews with tourism operators in British Columbia. Sea otters were a strong factor in people's choices regarding wildlife viewing, and sea otters could have large benefits for local economies. Alongside socio‐economic characteristics, tourism experience influences tourists’ preferences. Tourism operators did not perceive sea otters as strongly influencing tourist choice, highlighting the gap that can occur between the perception and the reality of tourist preferences, leading to missed opportunities for the alignment of economic development with conservation actions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".