More than Magnitude: Towards a multidimensional understanding of unprecedented weather to better support disaster management
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 1900 Galveston Texas Hurricane, the 2021 Pacific Northwest heatwave, and the 2023 Tropical Cyclone Freddy were all events that were unprecedented in diverse ways and had severe humanitarian impacts. Understanding past and future risk of unprecedented weather is an emerging question across climate science disciplines but use of this research by the humanitarian sector has been limited. This cross-disciplinary paper is an effort by climate scientists and humanitarian practitioners to address this gap. For it, we combined narrative and scoping literature reviews with structured practitioner engagement to develop a working definition and typology of unprecedented weather through a disaster management lens. We qualitatively coded over 400 peer-reviewed articles to highlight the current state of research on unprecedented weather, and then discussed these findings in a workshop with 48 humanitarian practitioners. Our results show that, while analyses of past and future unprecedented weather often focus on the magnitude of such events, extreme weather can be unprecedented in many other dimensions, all which have significant implications for early warning, anticipatory action, and disaster response planning. We conclude with a call for more imagination and diversity in research on extreme weather risks, and for closer collaboration between climate scientists and disaster managers to design and answer questions that matter for humanitarian outcomes.
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 | 0.000 | 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 | 0.000 | 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