Incorporating First Nations Knowledges in Local Disaster Management Plans: A Comparative Analysis of 82 LGAs
Bibliographic record
Abstract
The socio-economic and human costs from disaster risks in Australia are forecast to rise steeply through 2050. It is increasingly urgent that individuals and communities develop skills, resiliency, and capacity to take action for better risk reduction and management over the long-term. This is critical for Indigenous communities in regional and remote locations who reside at the interfaces closest to sever disaster risks and who are often least able to respond rapidly. However, while research and international frameworks such as the Sendai Framework for Risk Reduction (2015-2030) have identified the necessity of inclusiveness for effective disaster management, policies and agencies have thus far failed to clearly integrate local and Indigenous contexts and practices into planning and response strategies. Better “listening” to, and learning from, Indigenous lived experiences is of paramount importance to move this agenda forward and facilitate the mental shifts required to mobilize policy and practice. As part of a larger project on disaster management in Australian Indigenous communities, several case studies were undertaken to identify trends in inclusive, community-led, disaster management methods. This paper presents preliminary findings using two of the cases as comparative examples of “better listening” – including a remote community in north island of New Zealand and a regional community in Victoria, Australia. The research was conducted using qualitative, semi-structured interviews during 2022-23; in-person and digitally. Transcriptions were thematically analysed using NVivo. The study found that the crucial elements for developing and implementing successful community-led disaster management were: to identify key community partners; to build long-term relationships of trust, genuine consultation and partnerships well before disasters strike through extensive “listening work”; listening to, and acting on, what the community needs while preparing for a disaster; and incorporating community-specific response and recovery plans through continuous engagement and revision of strategies in conjunction with genuine “listening”.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".