The Insuring Instinct in a Changing World
Bibliographic record
Abstract
The chapter recounts the history of the concept of risk and of early modern insurance practices as they evolved from communal attempts, mainly in Europe between the seventeenth and nineteenth centuries, to discipline and commodify earlier forms of speculation. The processes eventually involved in risk assessment, pooling, and diversification were neither inevitable nor entirely capricious. They were complicated and indeterminate, and accommodating structures of government developed in tandem. The chapter also provides an orientation to increasingly complicated scholarship focused on the governance of risk and uncertainty as considerations of their consequences migrate over time and geographic space. It notes how leading business firms around the world, not just in the insurance industry, came to base their strategies on sophisticated models that take for granted commonplace, and even simplistic, notions of risk. Those models are dynamic and they now shape larger structures within which firms and people interact. As collective impressions of risk become more complex, shared sources of uncertainty also rise, often because economic and political systems are misaligned.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.031 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".