Re-thinking Neutrality Through Emotional Labour: The (In)visible Work of Conference Interpreters
Bibliographic record
Abstract
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.
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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 | 0.020 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.044 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.014 |
| 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".