Ethnic minority livelihoods contesting state visions of 'ideal farmers' in Vietnam's northern borderlands
Bibliographic record
Abstract
Since the 1990s, several Vietnamese state policies have focused on whole-heartedly integrating upland ethnic minority farmers into the market economy. These policies revolve around interventions related to natural resource use, agricultural intensification, and cash-cropping, in a quest to produce 'ideal farmers.' Simultaneously, the growing frequency of extreme weather extremes has been impacting upland livelihoods in important ways. Consequently, farmers must now navigate an increasingly complex socio-political and natural environment when making livelihood decisions. This study focuses on a mountainous district in the Sino-Vietnamese borderlands. Through in-depth qualitative fieldwork with ethnic minority semi-subsistence farmers and local officials, we delve into the ways in which farmers respond to the unpredictable interplay of state interventions and extreme weather events. Rooted in contemporary political ecology debates, we adopt a multi-scalar approach while drawing on actor-oriented livelihood conceptualizations. Our findings show that the Vietnamese state has failed to convince upland farmers to fully commit to state-endorsed cash-cropping schemes. Yet, farmers do not necessarily reject such opportunities outright. Rather, they navigate and rework state-supported opportunities, all while remaining acutely attuned to local physical environment limits, important social networks, and cultural norms and expectations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| 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".