Remembering for resilience: nature-based tourism, COVID-19, and green transitions
Bibliographic record
Abstract
The COVID-19 pandemic had transformative effects on the tourism sector at an unparalleled scale. With the rapid onset of unprecedented travel restrictions, tourists were abruptly confined to experiences in their regional surroundings that led to new and refreshed relationships with local destinations. This paper draws on qualitative interviews with small tourism businesses in two distinct but proximate nature-based destinations in Ontario, Canada and considers how they responded to the COVID-19 pandemic. Findings are positioned within Holling's Adaptive Cycle to consider implications for ongoing resiliency planning for disturbances relating to climate change. Over a 2-year period (2020–2022), SMEs revealed that after an initially turbulent period they quickly adapted to the absence of international long-haul visitors by embracing a surge in domestic demand for nature-based, outdoor experiences. The paper contributes to the literature on tourism SMEs by connecting experiences of COVID-19 to resiliency planning for future predictable disturbances. Two critical lessons for enhancing destination resiliency are identified: engagement of regional tourism demand, and destination level leadership, through investment in infrastructure and partnerships, can both be harnessed to support SMEs and their communities in transitioning toward a more sustainable, resilient and climate-friendly tourism future. Given the growing demand for tourism businesses to transition away from environmentally harmful practices and a longstanding dependency on economic growth, these resources can help destinations enhance preparedness for future changes to tourism flows driven by decarbonization scenarios and increased climatic impacts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".