Waiting for the market? Microinsurance and development as anticipatory marketization
Bibliographic record
Abstract
This article traces experiments aimed at promoting wider adoption of 'microinsurance' - small, simplified insurance policies targeting the poorest. Microinsurance is a central element of a wider turn towards the promotion of 'resilience' in global development. The development of commercial markets for microinsurance, however, has failed to meet the expectations of promoters. This article traces the ways that the diverse donor agencies, professional organizations and philanthropic organizations involved in the promotion of microinsurance have responded to these failures, primarily by seeking to articulate basic data infrastructures that might make possible profitable insurance operations. These activities are described as a kind of 'anticipatory marketization' - experiments seeking to prepare the ground for the emergence of markets for risk management, thus far without much success. Where microinsurance has often been described in terms of 'financialization', this article suggests that there are important political dynamics at play that have been overlooked. Efforts to develop markets for microinsurance, and the persistent focus on troubleshooting and re-engineering those markets in the face of failure, are not driven directly by finance capital. Rather, they reflect fraught efforts to articulate modes of social protection not requiring substantial redistribution.
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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 | 0.006 | 0.008 |
| 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 | 0.022 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 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".