The Truncated Commercialization of Microinsurance and the Limits of Neoliberalism
Bibliographic record
Abstract
ABSTRACT Microinsurance — defined as low‐cost insurance products targeting low‐income populations — exemplifies key themes in contemporary neoliberalism, and has figured prominently in neoliberalism's turn to discourses such as ‘risk management’ and ‘financial inclusion’. The development of commercial markets for microinsurance, however, has in practice been highly variable and often very limited. This article considers the implications of this process of ‘truncated commercialization’. It draws on a Polanyian analytical framework that emphasizes the contradictory regulatory dynamics involved in the commodification of labour. The article applies this approach by tracing multiscalar efforts to promote microinsurance, examining the emergence of the concept in the efforts of the International Labour Organization to promote social security for informal workers in the 1980s and 1990s, looking at the adoption of explicitly commercializing imperatives in the work of the International Association of Insurance Supervisors in the 2000s, and, finally, considering a case study of South Africa. The truncated commercialization of microinsurance, it is argued, provides a useful lens through which to see the practical impossibility of neoliberal development strategies.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.038 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".