Ethnography in Translation Studies: an object and a research methodology
Bibliographic record
Abstract
Based on a review of the literature on ethnography produced by translation scholars over the past twenty years, this contribution explores how translation studies [TS] has appropriated this concept, first as a way to solve translation problems (with Eugene Nida), then as an object (within the cultural turn) and more recently as a research methodology to document and analyze translation and interpreting events in context. The author shows how, in the early seventies, both cultural anthropology and TS saw a change in paradigm that brought the two disciplines closer at the surface level (as the metaphor of culture as a text gained grounds), but that draw them very much apart from an epistemological viewpoint. Indeed, while ethnography was undertaking an interpretive turn, TS chose to define itself as an empirical discipline based on systematic and objective observation; this positivistic bias in early TS could partly explain its late adoption of ethnography as a research methodology. This literary review finally reminds us of the many dichotomies out of which TS has grown and structured itself — text vs context; translation vs. interpretation; experiential vs. scientific knowledge, hermeneutics vs. empiricism, to name but a few — and suggest the need for an interpretive move within the discipline.
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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 | 0.102 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.008 | 0.039 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".