Products, features and target markets: An inquiry into Aboriginal tourism product preference of visitors to northern British Columbia.
Bibliographic record
Abstract
Many Aboriginal communities in British Columbia, Canada and worldwide, are on the search for new development opportunities and industries to diversify their economies. Aboriginal tourism is one of these industries, often described as a development opportunity which may promote Aboriginal peoples' cultural revitalization and economic benefits without exploiting their traditional territories. While documentation of demand for Aboriginal tourism experiences in North America exists, visitor product preferences remain largely unknown. However, Aboriginal tourism business entrepreneurs and operators need this information in order to manage for the long-term profitability of their businesses. The study presented in this thesis attempts to provide insight into this topic. Working under the umbrella of a collaborative research effort between Tl'azt'en Nation and the University of Northern British Columbia, I administered a questionnaire containing four hypothetical Aboriginal tourism product descriptions and 31 individual features of Aboriginal tourism products to 337 visitors of northern British Columbia during the summer of 2007. I analyzed the data and established a profile of the travelers and their Aboriginal tourism product preferences. Based upon the identified primary features of Aboriginal tourism products I made several recommendations regarding tourism product development. Following this, I conducted a cluster analysis based on visitors' interest in these particular product features and in an attempt to segment the market for Aboriginal tourism in northern British Columbia. I identified three distinct clusters as the Culture Seekers, the Nature-Culture Observers and the Sightseers. The first two segments appear to hold the strongest potential for rural First Nation communities. In the end, which segment to target and which marketing channels to use will depend on Tl'azt'en and other First Nations' level of comfort in interacting with visitors and how much of their culture they are willing to share with touris
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".