Cardamom Casualties: Extreme Weather Events and Ethnic Minority Livelihood Vulnerability in the Sino-Vietnamese Borderlands
Bibliographic record
Abstract
In the wake of important economic reforms and an ongoing agrarian transition, non-timber forest products, most notably black cardamom, have emerged as significant trade options for ethnic minority farmers in the mountainous Sino-Vietnamese borderlands. Yet, after a series of harsh winters had already crippled cardamom harvests in the 2000s, extreme weather in 2016 decimated the cardamom plantations of hundreds of farming households. Drawing from sustainable livelihoods, livelihood diversification, and vulnerability literatures, we investigate the multiple factors shaping how these harvest failures have affected ethnic minority cultivator livelihoods. Focusing on four case study villages, two in Yunnan, and two in northern Vietnam, we analyse the coping and adaptation strategies Hmong, Yao, Hani, and Yi minority farmers have adopted. We find that farmers’ decisions and strategies have been rooted in a complex ensemble of factors including their degree of market access, other livelihood opportunities available to them, cultural traditions and expectations, and state development strategies. Moreover, we find that in recent years the Chinese and Vietnamese states have stood-by as affected cultivators have struggled to reorganize their livelihoods, suggesting that the impacts of extreme weather events might even serve state projects to further agrarian transitions in these borderlands.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".