The perfect storm: extreme weather events and speculation along cardamom commodity chains in Southwest China
Bibliographic record
Abstract
This article probes how extreme weather events in Southwest China in 2016 impacted actors in the black cardamom commodity chain. Harvest failures led to sudden supply disruptions, triggering a price spike that worsened an ongoing price bubble which had been driven by long-lasting speculative maneuvers from large market actors but was also beneficial to ethnic minority farmers. Extreme weather events created both risk and profit opportunities for farmers in the Sino-Vietnamese borderlands and Han traders located at nodes further along the commodity chain. We document how actors reacted to these circumstances, and analyze the factors that influenced their capacity (or lack thereof) to benefit from extreme weather event-driven market vagaries. We find that ethnicity, position, and role along the commodity chain, plus access to financial and social capital, are all involved. Access to information is key, but is distributed asymmetrically along the commodity chain; multidirectional trust relations allow larger market actors to gain and maintain greater access to market information than other stakeholders. We engage with scholarship on commodity chains, extreme weather events, and price spikes, arguing that these bodies of literature are seldom addressed together and that extreme weather events should receive closer attention as factors shaping power relations within commodity chains.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".