The Bovine Border at Mae Sot: “Just Transit” and the Live Cattle Trade Across Southeast Asia
Bibliographic record
Abstract
This paper examines the cross-border cattle trade along the Myanmar – Thailand border, where live cattle are transformed into “lively commodities” (Collard and Dempsey 2013) through a complex infrastructure of quarantine, classification, and care. Drawing on ethnographic fieldwork with cattle caretakers, traders, quarantine station owners, and veterinarians, this paper elucidates how cattle arrive as potential disease carriers and are rendered healthy, legal, and trade-ready through documentation and certification, culminating in the issuance of a bo(a)rdering pass that reclassifies them as Thai oxen. In parallel, cattle traders employ informal practices of sorting and valuation, relying on embodied expertise to anticipate an animal’s growth potential and market value. Migrant caretakers provide labor-intensive, affective care sustaining the animals’ health while in quarantine. These intersecting practices produce the “square,” meaty ox, a figure of commodity perfection destined for sale in China. The paper argues that cattle mobility does not simply traverse the border but actively participates in its making, reshaping its function, infrastructure, and significance. Conversely, the border transforms the meaning, legal status, and the physiology of the animals that pass through it. The entanglement of animals, humans, and border practices give rise to and enact a bovine border that is not merely crossed but also continually reassembled.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".