Interpreting the Human Rights Field: A Conversation
Bibliographic record
Abstract
Abstract This article takes the form of a conversation between an anthropologist and seven interpreters who worked for the UN Office of the High Commissioner for Human Rights (OHCHR) during its mission in Nepal (2005–2011). As any human rights or humanitarian worker knows quite well, an interpreter is essential to any field mission; they are typically the means by which ‘internationals’ are able to speak to any local person. Interpreters make it possible for local events to be transformed into a globally legible register of human rights abuses or cases. Field interpreters are therefore crucial to realizing the global ambitions of any bureaucracy like the UN. Yet rarely do human rights officers or academics (outside of translation studies) hear from interpreters themselves about their experience in the field. This conversation is an attempt to bridge this lacuna directly, in the hope that human rights practitioners and academics might benefit from thinking more deeply about the people upon whom our knowledge often depends.
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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 | 0.090 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.062 | 0.085 |
| Scholarly communication | 0.032 | 0.042 |
| Open science | 0.005 | 0.030 |
| Research integrity | 0.030 | 0.050 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".