Incorporating First Nations knowledges into disaster management plans: an analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it