Derivatives for Development? Small‐Farmer Vulnerability and the Financialization of Climate Risk Management
Bibliographic record
Abstract
While agricultural production has always been a risky endeavour, it has become even more so in the current context of climatic change and increasing market uncertainty. Meanwhile, the rollback of state protections has rendered small‐scale farmers, especially marginalized peasant producers in the Global South, particularly vulnerable to these contemporary stressors. This essay critically evaluates the contemporary roll‐out of financial derivatives that purportedly aim to mitigate smallholder vulnerability. It gives particular attention to a novel type of derivative known as index‐based agricultural insurance (IBAI) that plays an increasingly prominent role in initiatives to ‘climate proof’ agriculture. The creation of IBAI markets has required significant work, including (1) technical interventions to debundle environmental risk from agricultural production and rebundle it in novel ways that support private financial capital and agricultural input suppliers, (2) extensive state support in the creation of risk markets, and (3) the construction of an accommodating ‘insurance culture’ among small‐scale producers. In addition to mitigating weather‐based risk, a primary objective of IBAI is to spur agricultural modernization. In promoting this agenda, IBAI initiatives may have the paradoxical effect of exposing smallholders to new risks while expanding their overall vulnerability to environmental and economic stressors.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".