Bibliographic record
Abstract
Abstract Climate, whether warm and sunny or cold and snowy, can complement tourism by providing favourable conditions for visitors and their desired activities. As a result, climate is often a main resource upon which tourism destinations depend. Extreme or unpredictable weather may be viewed as unfavourable for attracting visitors. However, even seemingly inclement climate conditions can be used as a resource in tourism. Existing research does not demonstrate how outwardly negative climate conditions might influence destination image, destination selection, and local tourism development. This case study examines how cold climates present both a challenge and an opportunity to developing and promoting an area for tourism. Here we outline how The Forks National Historic Site, located in Winnipeg, Manitoba and a popular summer destination was successfully re-framed as a cold-weather attraction. This case study represents a specific instance of how a tourism destination may highlight a unique feature, such as a frozen river. Readers will understand how harsh weather can become an uncommon resource that facilitates tourism and recreation and enhances overall visitor experience. As such, this case study presents a specific example of how various stakeholders came together to offer a unique experience and transformed an otherwise negative climate condition into a positive and desirable aspect of the destination. VIU logo WLCE logo Information Vancouver Island University World Leisure Centre of Excellence © A.J. Johnson and C. Van Winkle, 2015
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 | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".