COVID-19’s attack on tourism and the adventure communities that defended it
Bibliographic record
Abstract
The COVID-19 pandemic has presented unprecedented uncertainty for the health, safety, and wellbeing of tourism dependent communities. In the first two years of the pandemic, it was estimated that COVID-19 led to four trillion United States Dollars in losses in GDP to the global tourism economy (UNCATD, 2021). The goal of this research is to understand how adventure tourism stakeholders in British Columbia’s (BC) Thompson Okanagan tourism region were affected by the pandemic. Results from this study include insight into the vulnerabilities and adaptation strategies of adventure tourism stakeholders and highlight cases of resilience in the Thompson Okanagan tourism region. This research was completed in partnership with the Thompson Okanagan Tourism Association (TOTA), a Canadian non-profit that represents and supports regional business and community tourism interests (TOTA, 2022). A collaborative research design with TOTA was used because it enabled the direct incorporation of tourism stakeholders’ needs into the research design and facilitated the rapid dissemination of results to key regional decisionmakers. The mixed-method exploratory research was successful in identifying impacts of COVID-19 on BC adventure tourism, barriers and limitations for response, strategies for navigating uncertainty, the importance of client and community relations, and pandemic-driven adaptations and innovations. Destination stakeholders in Thompson Okanagan adventure communities demonstrated a high degree of resilience to the onset of COVID-19. Key resilience findings from this study include adventure operators’ ability to strategize and reframe challenges into opportunities, the importance of diversified tourism markets, the impact of proactive communication among value networks, and an understanding of the relationship between sustaining the environment and sustaining adventure recreation. The results from this case study can be used to identify tourism vulnerabilities as well as areas of opportunity for resiliency and sustainability development.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".