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Record W3169151929 · doi:10.7202/1077714ar

Re-thinking Neutrality Through Emotional Labour: The (In)visible Work of Conference Interpreters

2021· article· en· W3169151929 on OpenAlexvenueno aff
Irem Ayan

Bibliographic record

VenueTTR traduction terminologie rédaction · 2021
Typearticle
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsInterpreterNeutralityContext (archaeology)Perspective (graphical)Emotional laborSociologyFeelingSocial psychologyPsychologyEpistemologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Relying upon a combination of ethnomethodological and sociological tools provided by Hochschild’s (2003 [1983]) theory of emotional labour, this article examines the concept of the interpreter’s neutrality as a form of feeling management at work which requires interpreters to align their behaviours with the norms shaping each interpreting setting. Drawing on the interviews conducted between March and August 2018 with twenty-one interpreters working in various social, cultural, and institutional settings, the study describes the interpreter’s emotional labour in the context of conference interpreting, and argues that the interpreter’s actual task of becoming the voice of the speaker intrinsically involves emotional labour. Achieving neutrality entails suppressing personal beliefs and displaying certain emotions which may not always be genuine in some contexts, and tending to the needs of clients in others. The conceptual framework of emotional labour offers an important analytical tool to re-visit theoretically and empirically not only the notion of the interpreter’s neutrality from a critical perspective, but also the incoherence of professional codes of practice, which oftentimes leaves interpreters in a practical and ethical quandary.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.179
Threshold uncertainty score0.619

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.191
GPT teacher head0.442
Teacher spread0.252 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations11
Published2021
Admission routes1
Has abstractyes

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