Early warning information seeking in the 2009 <scp>V</scp>ictorian <scp>B</scp>ushfires
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
This study examines early warning from the users' perspective as a special category of information seeking. Specifically, we look at the 2009 V ictorian bushfires in A ustralia as an instructive case of early warning information seeking. The bushfires, the worst in A ustralia's recorded history, were unique in its ferocity and damage caused, but also in the amount of data and research that was generated. We analyzed the affected residents' information needs, seeking and use in terms of their cognitive, affective, and situational dimensions. We found that residents wanted information that would act as a “trigger for action,” provide timely warning, and indicate clearly fire severity. Nearly two thirds of residents surveyed did not receive an official warning. Almost half first found out that the bushfire was in their area through personal observation of smoke, embers, or flames. We suggest that a form of normalcy bias may have been at work during information seeking, causing people to interpret their situations as “normal” even when disaster warnings have been issued. Although the authorities had adopted a “Stay or Go” policy to help residents use warning information to decide between staying to defend their property or leaving early, the policy's effectiveness was undermined by information challenges.
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 | 0.012 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.010 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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