The impostor syndrome: language barriers in organizational ethnography
Bibliographic record
Abstract
Purpose The use of organizational ethnography has grown significantly during the past decades. While language is an important component of ethnographic research, the challenges associated with language barriers are rarely discussed in the literature. The purpose of this paper is to open up a discussion on language barriers in organizational ethnography. Design/methodology/approach The author draws on her experience as a PhD student doing an organizational ethnography of an emergency department in a country where she initially did not speak the local language. Findings The paper examines the author's research process, from access negotiation to presentation of findings, illustrating the language barriers encountered doing an ethnography in parallel to learning the local language in Sweden. Research limitations/implications This paper calls for awareness of the influence of the ethnographer's language skills and shows the importance of discussing this in relation to how we teach and learn ethnography, research practice and diversity in academia. Originality/value The paper makes three contributions to organizational ethnography. First, it contributes to the insider/outsider debate by nuancing the ethnographer's experience. Second, it answers calls for transparency by presenting a personal ethnographic account. Third, it contributes to developing the methodology by offering tips to deal with language barriers in doing ethnography abroad.
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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 | 0.174 | 0.389 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.010 | 0.051 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".