Knowledge mobilization, wildfire risk, and sustainable tourism in UNESCO biosphere reserves
Bibliographic record
Abstract
Wildfires significantly affect nature‑based tourism (NBT) by reducing park visits, degrading visitor experiences, harming regional economies, and increasing public health and safety costs for emergency response. This paper examines these impacts, with a focus on future climate change risks, through a mixed methods case study on the Niagara Escarpment Biosphere Reserve in the province of Ontario, Canada. The study uses statistical analysis of survey data and thematic coding of interviews with key informants to identify strengths, weaknesses, and opportunities in knowledge mobilization (KMb) for wildfire risk management. Key findings include strengths such as transparent communication and integration of natural science in decision‑making, but also weaknesses like limited collaboration with Indigenous communities and a clear lack of understanding of the role of social science in risk management. Additionally, the study highlights the need for greater public health sector involvement and more financial resources to support risk preparedness and response. The paper demonstrates the interconnectedness of knowledge management, risk management, and tourism geographies. It concludes by detailing the ways in which the UNESCO Biosphere Reserve model can be used to better understand the spatial and informational dimensions of risk management and tourism, as well as facilitate collaborations across scales, sectors, and disciplines.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".