The craft of translation: documentary practices within immigration advocacy in the United States
Bibliographic record
Abstract
Abstract This article builds on anthropological research on bureaucratic inscription as a power‐laden process to explore the craft of document translation in contexts of immigration legal advocacy. In a legal climate characterized by suspicion and resource scarcity, immigrants who seek to regularize their status in the United States face steep evidentiary challenges, including the requirement that all documentation, including records from their countries of origin, letters of support from friends and family, and their own affidavits, must be translated into English. Approaching immigration document translation ethnographically and drawing on multi‐year fieldwork in a nonprofit providing legal services to low‐income, Spanish‐speaking immigrants, this article focuses on translation as neither straightforward and mechanical nor as impossibly complex but rather as a craft that involves exercising discretion. Practicing this craft with care is one way to counter the otherwise alienating and state‐centric nature of bureaucratic inscription.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".