Understanding and improving community flood preparedness and response: a research framework
Bibliographic record
Abstract
Many social research projects identify issues with community disaster preparedness and response but struggle to attribute these issues to underlying causes and recommend possible ways to address them. A research framework that considers the underlying causes of preparedness and response and possible interventions was developed for the Wimmera region of Victoria, Australia. The research framework was developed in conjunction with the Wimmera Catchment Management Authority and tested in a social research project across 6 communities in the Wimmera region. This paper provides an outline and rationale for the components of the research framework. It also summarises the regional flood insight afforded by the research framework. The research framework, albeit with some limitations, has universal appeal not only in the examination of community flood preparedness and response, but also for other hazards and other parts of the disaster management cycle.
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 | 0.068 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.012 | 0.048 |
| Scholarly communication | 0.023 | 0.023 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".