Interpreting the Human Rights Field: A Conversation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.003 | 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 | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it