Managing the socioeconomic impacts of extreme weather events in the southwest pacific basin
Bibliographic record
Abstract
Cyclones are a recurrent feature in the Southwest Pacific basin. Australia, Papua New Guinea, Fiji and New Zealand are responsible for monitoring and forecasting cyclone activity in the region. In dealing with extreme events of this nature, the small islands and atolls have had to depend heavily on their more developed neighbours for assistance. Under the FRANZ and Pacific Island Forum (PIF) arrangements Australia, New Zealand and France have an agreement in place to assist partner countries when such disasters strike. The last category five cyclone (Australian scale), to make landfall in this region was Cyclone Winston, which devastated the Island of Fiji in 2016. Fiji is one of the 16 independent member States of the PIF, which includes Australia and New Zealand. Following the Hyogo Framework for Action 2005-2015, an outcome of the World Conference on Disaster Reduction held in January 2005, the PIF convened a meeting, in Madang, Papua New Guinea, to develop a regional Framework for implementation. Foremost in mind were the social and economic structures of these countries where entire communities could lose their livelihoods or face severe disruption as a result of one disaster. This paper explores disaster management, of the impact of severe cyclones, by these PIF countries (excluding Australia and New Zealand), specifically in terms of disaster prevention, preparedness, relief and recovery. Specific focus will be on the islands of Fiji, Tonga and Vanuatu, where category five cyclones made landfall within the last three years and Samoa, where a category four cyclone made landfall in 2012.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".