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
Abstract Catastrophe (CAT) bonds are a means to share economic losses resulting from natural disasters including hurricanes, earthquakes, and—more recently, as used by the World Bank—pandemics. Because these pandemic CAT (PCAT) bonds were subsidized by government donations and, absent the occurrence of covered pandemics, were full recourse to the World Bank, this precedent was not market‐tested for the commercial viability of such instruments. This paper examines PCAT bonds as a means of securitization for pandemic risk through the lenses of reinsurers′ unmet capital needs and the requirements for the potential viability of a PCAT‐bond market. Where the reinsurance market has limited capacity to either absorb or spread the risks of global‐level CATs, risk securitization may be effective for layered risk sharing. The authors explore whether pandemic risk may be insurable by increasing reinsurance capacity to handle losses from business interruptions that are due to unintentional pandemics. Are PCAT bonds a potential means to achieving this protection? Historically, insurers were reluctant to enter this market because the required spreads and associated bond‐issuance expenses were considered prohibitively high. The situation has improved, although there remains much room and need for growth in this market.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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