The Truthiness about Hurricane Catastrophe Models
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
In recent years, US policy makers have faced persistent calls for the price of flood and hurricane insurance cover to reflect the true or real risk. The appeal to a true or real measure of risk is rooted in two assumptions. First, scientific research can provide an accurate measure of risk. Second, this information can and should dictate decision-making about the cost of insurance. As a result, contemporary disputes over the cost of catastrophe insurance coverage, hurricane risk being a prime example, become technical battles over estimating risk. Using examples from the Florida hurricane rate-making decision context, we provide a quantitative investigation of the integrity of these two assumptions. We argue that catastrophe models are politically stylized views of the intractable scientific problem of precise characterization of hurricane risk. Faced with many conflicting scientific theories, model theorists use choice and preference for outcomes to develop a model. Models therefore come to include political positions on relevant knowledge and the risk that society ought to manage. Earnest consideration of model capabilities and inherent uncertainties may help evolve public debate from one focused on “true” or “real” measures of risk, of which there are many, toward one of improved understanding and management of insurance regimes.
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.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
| gpt | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 it