Bibliographic record
Abstract
Fourth Volume in the Studies in Interpretation Series The work of interpreters in legal settings, whether they are spoken or signed language interpreters, is filled with enormous complexity and challenges. This engrossing volume presents six, data-based studies from both signed and spoken language interpreter researchers on a diverse range of topics, theoretical underpinnings, and research methodologies. In the first chapter, Ruth Morris analyzes the 1987 trial of Ivan (John) Demjanjuk in Jerusalem, and reveals that what might appear to be ethical breaches often were no more than courtroom dynamics, such as noise and overlapping conversation. Waltraud Kolb and Franz Pochhacker studied 14 asylum appeals in Austria and found that interpreters frequently aligned themselves with the adjudicators. Bente Jacobsen presents a case study of a Danish-English interpreter whose discourse practices expose her attempts to maintain, mitigate, or enhance face among the participants. In the fourth chapter, Jemina Napier and David Spencer investigate the effectiveness of interpreting in an Australian courtroom to determine if deaf citizens should participate as jurors. Debra Russell analyzed the effectiveness of preparing sign language interpreter teams for trials in Canada and found mixed results. The final chapter presents Zubaidah Ibrahim-Bell s research on the inadequate legal services in Malaysia due to the fact that only seven sign interpreters are available. Taken together, these studies point to a coming of age of the field of legal interpreting as a research discipline, making Interpreting in Legal Settings an invaluable, one-of-a-kind acquisition.
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 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".