Connecting community organisations for disaster preparedness
Bibliographic record
Abstract
After fires swept through the lower Blue Mountains of NSW in October 2013 and destroyed over 200 homes, a research project was initiated and titled 'Community Connections: Vulnerability and Resilience within the Blue Mountains'.Reported in this article are the results of eight in-depth interviews conducted with local community leaders.They were asked to reflect on their leadership experiences before, during and after the fires.The research clearly demonstrates that prior to the fires there were no formal connections between local emergency services and local community organisations.Each had limited knowledge of the other in terms of skills, capacities, scope and available resources.This article will elaborate on the lessons learned by the community leaders interviewed.Just as collaborative bonds were finally being formed and combined initiatives had begun to bear solid results -reflected in higher levels of householder disaster preparedness, recovery funding ran out.This article highlights the lessons learned, and includes the importance of maintaining a formalised and continuous connection between emergency services and community organisations.The research recommends that disaster preparedness be embraced as a part of 'core business' by community organisations, and that multi-stakeholder connections be forged and strengthened through collaborative community engagement initiatives at the level of local disaster planning and preparation.Both recommendations contribute to the paradigm shift anticipated by Australia's 'National Strategy for Disaster Resilience'.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.022 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".