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Record W3131961684 · doi:10.1108/joe-01-2021-0003

The impostor syndrome: language barriers in organizational ethnography

2021· article· en· W3131961684 on OpenAlexaff
Virginia Rosales

Bibliographic record

VenueJournal of Organizational Ethnography · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsEthnographyInsiderSociologyOriginalityNegotiationSocial scienceQualitative researchPolitical scienceAnthropologyLaw

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.007
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.045
GPT teacher head0.427
Teacher spread0.383 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2021
Admission routes1
Has abstractyes

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