Filtered meaning: appreciating linguistic skill, social position and subjectivity of interpreters in cross-language research
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.
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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 | 0.071 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.018 | 0.063 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.003 | 0.005 |
| 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 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".