Pets are family, keep them safe: a review of emergency animal management in remote First Nations communities
Bibliographic record
Abstract
Planning for and considering animals is a growing area within emergency and disaster planning. As people adapt to the changing risks of disaster events that are increasing in magnitude and frequency, communities, particularly those in regional and remote areas of Australia, face challenges that are very different from other more populated areas. These communities are often home to pets, which pose unique challenges during evacuation, response and recovery phases of emergency management. Australian state and territory government emergency management plans give varied considerations to animal management. In the Northern Territory, the Territory Emergency Plan (Northern Territory Government 2022) serves as a base for animal management in disasters. However, significant reform is required to fill gaps in considerations of animals in remote communities, especially First Nations communities, given the strong socio-cultural connections within family structures and contributions to wellbeing under First Nations health worldviews and the human-animal bond. Such reform requires consultation and collaboration with First Nations Australians to promote ‘right-way’ science, build local capacity and support community resilience. Considerations of the interplay between people and their pets in disaster planning, response and recovery contributes to ongoing advances in the ‘One Health’ and ‘One Welfare’ paradigms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".