Bear-Viewing Ecotourism in British Columbia: Ecological, Economic, and Social Perspectives Using a Case-Study Analysis of Knight Inlet Lodge, BC
Bibliographic record
Abstract
Following a worldwide pattern of rapid ecotourism growth, British Columbia's wildlife-viewing industry is poised to expand in the near future. Using a case study example of Knight Inlet Lodge, the province's first and to date only destination for viewing grizzly bears (Ursus arctos) in the wild, I examine three criteria for sustainability that may help determine the short- and long-term direction and success of this industry: economic viability, ecological sensitivity, and cultural appropriateness. A high demand for ecotourism and wildlife viewing, both worldwide and in British Columbia in particular, is tempered by the potential economic pitfalls of ecotourism and the difficulties of calculating the value of viewed species and habitats. Nonetheless, an economic analysis of Knight Inlet Lodge and comparable locations in Alaska reveals a high demand and income potential for bear viewing in British Columbia. Numerous studies have demonstrated the potential for ecotourism and wildlife viewing to have an adverse effect on the species and habitats on which they depend. A literature review reveals the numerous ways in which this can occur on different types of targets, including bears, but also suggests ways to minimize this impact. Ecotourism's challenge of satisfying the needs and desires of both visitors and local communities, and ultimately enriching both in economic and cultural ways, begins with assembling baseline socioeconomic data. A survey of Knight Inlet Lodge guests, when compared to similar data on North American ecotourists and residents, indicates that visitors tend to be well-educated, financially secure, older, and concerned with the wellbeing of their natural surroundings and the animals they travel to view-both of which local communities tend to value highly as well.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".