International Sign conference interpreters as a Community of Practice
Bibliographic record
Abstract
Conference signed language interpreters traditionally work between their national sign language and the national spoken or written language. During their careers, interpreters may add other spoken or signed languages to their repertoire. Some also acquire International Sign (IS) as a working language and interpret between IS and other signed and spoken languages. Being highly context dependent, IS has limited conventions and there is no established educational path toward learning to interpret IS. Generally, the acquisition of IS by any signer happens through interaction with signers of other signed languages. In this article, we explore the concept of IS conference interpreters as a Community of Practice (CoP), where novices acquire and experienced IS interpreters further their IS interpreting competences through situated learning. Such learning in practice may ultimately lead to the development of interpreting expertise and expert performance in IS conference interpreting. We present new data from a 2019 global survey of IS conference interpreters and follow-up interviews with eleven selected survey respondents. The results of our study suggest that there is indeed a CoP of IS conference interpreters and that it is essential for individual interpreters to participate in that community to develop the required competences and professional practices.
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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 | 0.009 | 0.021 |
| 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 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".