Nature-Based Community Recovery Post-Natural Disaster: Black Summer Bushfires
Bibliographic record
Abstract
OBJECTIVE: Natural disasters can cause widespread death and extensive physical devastation, but also harmfully impact individual and community health following a disaster event. Nature-based recovery approach can positively influence the mental health of people and community's post-natural disasters. In response to the Australian bushfire season of 2019-2020, Zoos Victoria, in partnership with the Arthur Rylah Institute, worked with local communities in East Gippsland to support people's recovery through experiencing, supporting, and witnessing nature's recovery. METHODS: This mixed-method study explored how nature improved the recovery of remote and rural communities affected by the Black Summer bushfires in East Gippsland. The research studied the individuals' feelings about being involved in nature-based community events and their lived experiences. Data were collected from June to September 2023 through a nature-based community recovery project survey and community interviews. RESULTS: The findings demonstrated that engagement with natural environments promotes positive psychological, mental, and general well-being of people from bushfire-affected communities. Positive feedback from participants indicated the success of the Nature-Based Community Recovery Project in East Gippsland after the Black Summer bushfire. CONCLUSIONS: This research provides insights for future recovery projects and ensures that sustainable nature-based recovery solutions for bushfire-impacted communities can be established.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".