Bibliographic record
Abstract
The events of the last three years hâve provided some very painful lessons to the insurance industry and its customers, particularly regarding the size of the material and financial risks they face.Insur ers received their wakeup call at the bottom of the underwriting cycle, when the rates they offered were based upon an assumption of extraordinarily high investment yields.Today the soft market busi ness model is no longer viable.Already during 2000/01 renewals, it was évident that the soft market phase was coming to an end.Rates for commercial business and reinsurance began to rise slowly as equity markets peaked.The once constant capital gains income stream not only ran dry, but tumed very sour.However, it was the tragic and shocking event of 11 September 2001, in which 3,000 people lost their lives and 2,500 were injured that finally catapulted rates in most risk categories.Insured damage amounted to approximately USD 40 billiontwice the amount of Hurricane Andrew in August 1992, the second largest insured single event.The insurance industry was called to pay for a hitherto unimaginable risk in a dimension they had not calculated.From a financial perspective, the terrible spectre of 11 September did not represent the greatest burden for the insurance industry.Rather, it was the fall of equity markets world-wide that destroyed USD 100 billion in the non-life industry alone.Life insurers could hâve lost even more on their asset portfolios; however they offloaded
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.
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".