Global Challenges and the Frontier of Insurability
Bibliographic record
Abstract
The chapter examines global risks that are exceedingly complex and characterized by the long time horizons entailed in their governance. It argues that the dynamics of climate change, biodiversity loss, pandemics, and other system-spanning challenges are now forcing pragmatists and skeptics alike to push their thinking beyond the kinds of experiments in risk governance discussed in previous chapters. They suggest the need for profound socioeconomic transformation, eventually forcing deep structural political change at the system level. Complex and slow-moving crises with transnational dimensions will not be managed successfully by nation-states assigning priority to their own autonomy. The essential question comes back to the fore. Might the “insuring instinct” today be harnessed in zones that stretch the limits of risk calculation quickly enough to sustain more ambitious forms of collaborative governance? More specifically, can existing political authorities in vital and inherently complex policy arenas effectively deploy insurance narratives to move beyond voluntary and reversible intergovernmental arrangements without provoking self-defeating backlashes? The chapter reviews current analyses of key cases where private insurance reach their limits, but insurance metaphors promise to be politically useful.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".