Strengthening Destinations’ Resilience from Bushfires—A Study of Eastern Australia
Bibliographic record
Abstract
Climate change has brought people’s attention in recent decades, which demonstrates a critical phenomenon of increased natural disaster risks. The consequences of natural hazards are highly potential to bring significant economic, reputational, social, and environmental impacts on Australia’s tourism industry. Considering the close relationship between the unique natural environment and the local tourism industry, natural disasters always play critical roles in terms of the destinations’ resilience. This paper aims to examine the cause-and-effect of natural disaster resilience for the tourism industry in Eastern Australia with the particular concern of bushfire. Representative bushfire events will be studied to locate the industry’s preparedness and the existed action gaps mainly with the focus on government and destination management organizations, as well as discuss the disaster prevention implications, direct/indirect impacts and tourism-related issues. Also, a natural disaster resilience assessment framework for the industry will be developed with the key indicators from multiple aspects. A couple of future directions will be proposed regarding recovery methods, including the needs of destination image recovery, supportive policies for small businesses and cross-functional partnership.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".