Risk, religion, and reified camels: the past and the future of insurance in Iran
Bibliographic record
Abstract
This paper is a contribution to the understanding of insurance and risk management as a culturally mediated form of social action. It examines how commercial insurance functions in the Iranian marketplace, focusing on insurance against liability in bodily harm. The quantum of damages (diya) currently used in Iran’s ‘Islamicized’ laws, including in insurance law, is rooted in the historical conventions of Bedouin Arabs, chief among which was the cultural use of camels as a benchmark for the valuation of human life. The imposition of this cultural rule on contemporary insurance contracts complicates risk management via probabilistic logic, because there are major fluctuations in camel valuations in the Iranian market today. The use of this benchmark also has significant socio-economic implications for the insured. But, at the same time, there is surprising synergy between ancient culture and modern business because the same traditions that established camels as the benchmark for diya also gave birth to one of the earliest forms of proto-insurance against liability and continue to give religious legitimacy to the insurance business. By examining the incorporation of the Islamic quantum of damages in insurance calculations, this paper contributes to the broader understanding of the cultural and moral underpinnings of insurance, and sheds light on how the tensions between the cultural and economic elements of risk management are managed.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".