Entangled, unraveled, and reconfigured: Human–animal relations among ethnic minority farmers and water buffalo in the northern uplands of Vietnam
Bibliographic record
Abstract
In the rural rice fields of upland northern Vietnam, Hmong and Yao ethnic minority farmers have been relationally “entangled” with a number of domesticated animal species to secure semi-subsistence livelihoods. Among these different inter-species entanglements, the relationships between farmers and water buffalo are the most profound. However, in recent years, the broader, contextual factors that shape the entanglements between farmers and water buffalo have been changing rapidly, provoked primarily by increasing extreme weather events, government-supported market integration, and rising land constraints. As these environmental, political, and socioeconomic factors have intensified, the complexity and persistence of long-standing entanglements between farmers and water buffalo appear to be diminishing. We offer a new conceptual perspective to the entanglement literature in this regard, suggesting that “unraveling” might best represent these processes. Nonetheless, we present the idea of “resistant” entanglements to indicate how many farmers have halted unraveling processes, while we posit a future of “reconfigured” entanglements, increasingly based on market forces. Drawing from in-depth ethnographic fieldwork with ethnic minority farmers, we analyze the changing characteristics of these farmer–buffalo entanglements, as well as a range of related socioeconomic and cultural consequences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".