At the frontiers of digitization and the financialization of risk: The global politics of InsurTech
Bibliographic record
Abstract
Abstract Digital innovations in insurance, ‘InsurTech’, bring together two transformational forces in our contemporary world – risk and digitization. InsurTech has been celebrated and criticized. A literature on the social studies of insurance provides valuable and more nuanced insights into the social, cultural, and technological properties of InsurTech but it tends to analyze these at the firm level. This article brings together themes from assemblage and international political economy theories to integrate analysis of the structure of the global industry and the role of cross-border regulatory arrangements with the firm-level insights of the social studies of insurance literature. The article examines differentiation in the industry structure between stages of the insurance value chain, between incumbent and start-up insurers and Big Tech, and across jurisdictions and regions. It also examines the most globally significant regulatory responses to InsurTech: from the International Association of Insurance Supervisors, the European Insurance and Occupational Pensions Authority, the China Banking and Insurance Regulatory Commission, and the US National Association of Insurance Commissioners. It shows that the nuance and ethical content that is evident at the firm level in the social studies of insurance literature is interacting with similar nuance and ethical content in global regulatory arrangements.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.040 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".