Incorporating First Nations knowledges into disaster management plans: an analysis
Bibliographic record
Abstract
The Sendai Framework for Disaster Risk Reduction 2015-2030 (UNDRR 2015) advocates for incorporating Indigenous knowledges and practices to complement scientific knowledge for effective and inclusive emergency and disaster management. Such traditional and local knowledge is an important contribution to developing strategies, policies and plans tailored to local contexts. A comparative analysis of local disaster management plans in Australia was undertaken as part of a larger project on emergency and disaster management in Indigenous communities and was performed to benchmark against the Sendai Framework priorities. A comprehensive search of publicly available local disaster management plans and subplans in selected local government areas was undertaken. Eighty-two plans were identified as well as 9 subplans from a list of Indigenous communities and associated local government areas. This study found a wide disparity in the organisation, presentation and implementation of knowledges and practices of local communities. While some plans included evidence of engagement and consultation with members of local communities, overall, there was little evidence of knowledges or traditional practices being identified and implemented. This analysis was conducted during the COVID-19 pandemic (2020–21) and most councils had local pandemic management subplans. However, many were not publicly available and targeted approaches for Indigenous communities were not evident on council websites. To reflect the priorities of the Sendai Framework, better consultation with local communities and leaders at all levels of government needs to occur and subplans need to be easily available for review by policy analysts and academics.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".