Insurance Risks as Fictitious Commodities: The Institutional Constitution of Insurance Markets
Bibliographic record
Abstract
This dissertation aims at shifting the focus in the emerging sociology of insurance literature from the epistemic and cultural construction of private insurability towards the political-legal constitution of insurance markets. It investigates why the availability of actuarial data and/or extra-actuarial knowledge of risks are insufficient conditions for the functioning of the private insurance business. Drawing on Karl Polanyi’s institutional analysis, I develop a theory of the institutional constitution of markets and the concept of insurance risks as fictitious commodities, in order to analyse insurance markets as politically and legally constituted institutions. Following evidence that the private insurance governance cannot self-sufficiently ensure its profitability through contractual incentives and private controls of the insured, I claim that the institutional organization of substantive uncertainties as fictitiously commoditized insurance risks accounts for the basic determinant of private insurability. From the Polanyian perspective, the uncertainties experienced by individuals and threatening social provisioning, nature, and the market economy represent the raw materials which insurers capitalize. While the incentives insurers deal with prevent them from seeking the removal of these uncertainties, private insurers cannot autonomously ensure the profitable reduction, control and configuration of the covered uncertainties. To support these arguments, this dissertation first presents an historical analysis of the construction of insurance law in England from 1664 to 1774, demonstrating that the private insurance market proved unstable and generated uncertainty from its inception. The dissertation then presents a central, contemporary case study of the institutional constitution of the Canadian prescription drug insurance market in the context of the arrival of the new high cost specialty medicines. I reach two key findings. First, insurance capitalization itself generates instabilities and hence depends on the fictitious commoditization of substantive uncertainties into insurance risks by virtue of political-legal and non-competitive reduction and control of the underlying uncertainties covered as capital. Second, the fictitious commoditization process can involve the decommoditization of “bad” insurance risks by their distribution across the diversity of the institutional modes of economic integration. The Polanyian framework thus empirically examines how insurance capitalization of substantive uncertainties can impact individuals, institutions, and the citizenry.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.056 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".