Betrayal – Vice or Virtue? An Ethical Perspective on Accuracy in Simultaneous Interpreting
Bibliographic record
Abstract
Simultaneous conference interpreting represents a highly complex linguistic task and a very delicate process of information transfer. Consequently, the notion of truth – which applied to the field of simultaneous interpreting entails an accurate rendition of the original message – is of pivotal importance. In spite of that, an analysis of experimental transcripts and corpora sometimes seems to suggest that interpreters betray the speaker by deliberately altering the original. While we cannot exclude that such instances do exist, we argue that sometimes what looks like betrayal may in fact be a rendition based on a sound ethical decision. In this paper we take a closer look at these situations in an attempt to shed more light on the potential motivations underlying the interpreter’s decisions and actions. Using examples from real life interpreting situations, we take the interpreter’s output and put what at first sight appears to be a betrayal of the speaker on the ethical test bench, both from a deontological and a teleological perspective. Based on this analysis we propose a model suggesting that the interpreter uses three principal message components, verbal, semantic and intentional, in order to come up with an accurate interpretation of the original, which we call “truthful rendition.”
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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 | 0.061 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.075 |
| Scholarly communication | 0.014 | 0.024 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".