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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".