Training Interpreters in Legal Settings: Applying Role-Space Theory in the Classroom
Bibliographic record
Abstract
[About the book] Linguistic minorities are often severely disadvantaged in legal events, with consequences that could impact one’s very liberty. Therefore, training for interpreters to provide full access in legal settings is paramount. In this volume, Jeremy L. Brunson has gathered deaf and hearing scholars and practitioners from both signed and spoken language interpreting communities in the United States, Canada, and the United Kingdom. Their contributions include research-driven, experience-driven, and theoretical discussions on how to teach and assess legal interpreting. The topics covered include teaming in a courtroom, introducing students to legal interpreting, being an expert witness, discourses used by deaf lawyers, designing assessment tools for legal settings, and working with deaf jurors. In addition, this volume interrogates the various ways power, privilege, and oppression appear in legal interpreting. Each chapter features discussion questions and prompts that interpreter educators can use in the classroom. While intended as a foundational text for use in courses, this body of work also provides insight into the current state of the legal interpreting field and will be valuable to scholars, practitioners, and consumers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 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; both teacher heads agree on what is shown here.
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".