Spices as the saviour? The complex vulnerabilities of three commodity crop booms and ethnic minority livelihoods in Yunnan's agrarian frontier
Bibliographic record
Abstract
Commodity crops are redefining land use and rural smallholder livelihoods across Asia. These crops often have boom‐bust cycles with important implications for the drivers of farmer entry into and exit from particular cash crop opportunities. This paper offers a comparative analysis of the boom‐bust processes of three popular spice crops cultivated in Yunnan Province, southwest China. Drawing from agrarian frontiers and rural livelihoods literature, we disentangle the vulnerability contexts associated with black cardamom, cinnamon and star anise production, finding that farmers cultivating these spices face a combination of interlocking forms of vulnerability. Despite the monetary potential that each crop offered during its ‘boom’, the associated environmental, economic and political vulnerabilities caused most farmers to exit their production, responding with myriad on‐farm and off‐farm diversification strategies. Using a multi‐sited ethnographic approach, we draw on 52 in‐depth interviews with ethnic minority cultivators, spice traders and local government officials to untangle the complexities associated with cash crop production in this agrarian frontier, the interwoven vulnerabilities that result in their ‘busts’ and the coping and adaptation strategies that smallholder farmers employ. Our findings underline the importance of disaggregating farmer vulnerabilities and the need for more nuanced policy responses to adequately support small‐scale farmers.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".