Signed Language Interpreter Education Programs in North America: A Descriptive Study
Bibliographic record
Abstract
The objective of this study is to provide interpreter education faculty, university administrators, accrediting bodies, stakeholders, interpreters, and students (current or prospective) a descriptive snapshot-in-time of interpreter education curriculum and programs in North America. This study expands upon work done in the late 1987-1990 and 2007-2009 to capture a descriptive snapshot-in-time of the preparation of signed language interpreters. Researchers anticipated learning how programs align their curricula with CCIE accreditation standards (whether they are accredited or not), how two- and four-year programs (including Canada) allocate faculty time and resources, and how student characteristics and support systems differ among programs. This study examined interpreting education programs (IEP) in the U.S. and Canada across five distinct areas: (a) university and unit, (b) faculty, (c) students, (d) curriculum and internship, and (e) accreditation. Data were collected via a Qualtrics online survey with 67 questions sent to 125 IEP program directors with 58 total usable responses (46% return rate).
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".