Bibliographic record
Abstract
Abstract Over the past few decades, tourism has become one of the mainstay industries for many communities. Salt Spring Island, an island the south of Vancouver Island, is no exception to the benefits of tourism. Sustainable tourism is a necessary and useful pattern to Salt Spring Island. Under this perspective, there are some challenges that exist on Salt Spring Island regarding the tourism industry. Seasonality and inconvenient transportation are the major challenges, for seasonality will cause unstable revenue and under-or unemployment issues, which will affect residents’ quality of life; the inconvenient transportation will not only influence the locals’ daily life, but also reduce tourists’ interest in travelling. The paper contains three sections: the challenges that exist on Salt Spring Island tourism industries, the measures are using on Salt Spring Island, and innovative approaches from outside the region to help Salt Spring Island to improve its tourism development and sustainability. Despite the local people’s efforts to come up with solutions to the issues, these solutions cannot solve the problems because these solutions do not address the root of the problems. Therefore, the authors suggest three innovations to solve these two problems, including building greenways and automatic rental bicycle system, creating a theme for events, and developing wellness tourism.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".