Simultaneous Conference Interpreting and a Supernorm That Governs It All
Bibliographic record
Abstract
The integration of the concept of “social norm” into research on conference interpreting dates back to the late 1980s (Shlesinger 1989). This paper will show that conference interpreting is governed by role-related normative expectations which ultimately can all be traced back to the metaphoric concept of interpreters as conduits. This metaphoric concept that can be found in so many of the extratextual (re)sources on conference interpreting (Toury 1995) is extremely binding for conference interpreters and can therefore be regarded as an omnipotent norm – a supernorm that governs it all. Not only professional bodies such as the International Association of Conference Interpreters (AIIC), probably the most influential and powerful norm-setting authority in the field, but also individual authoritative personalities play a key role in promoting this supernorm. It is only in recent years that this supernorm, which demands that interpreters passively channel a message from one side or party to the other and thus tries to prevent an interpreter’s agency, has been challenged by empirical research (Angelelli 2004; Diriker 2004; Zwischenberger 2013). The discussion on norms in simultaneous conference interpreting will be enhanced by some selected findings of a web-based survey which was conducted among AIIC members. The survey’s main objective was to find out whether and to what degree professional conference interpreters adhere to the supernorm so strongly advocated by their professional body.
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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 | 0.031 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.031 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".