Filtered meaning: appreciating linguistic skill, social position and subjectivity of interpreters in cross-language research
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
Arriving in a foreign country with little knowledge of local languages presents the researcher with significant linguistic challenges. Our in-country contacts may suggest potential interpreters for us to hire, but how do we know if these interpreters can fluently speak the languages of our participants? Can we, lacking fluency in local languages, understand when the social position and lived experiences of our interpreter modify the discourses we seek to analyse? Drawing from my human geography research experience in Uganda, this article aims to share strategies to assess the linguistic skills of the interpreter and to understand his or her social position and subjectivity. Uniquely, this paper highlights differences in interpretation and links these differences to the assistants’ social position and subjectivity, highlighting the need to acknowledge that meaning can be filtered by interpretation and requiring that critical reflection be broadened to encompass interpreters in cross-language research.
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 | 0.199 | 0.146 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it