Sign Language Vocabulary Development Practices and Internet Use Among Educational Interpreters
Bibliographic record
Abstract
Sign language interpreters working in schools often face isolation in terms of their sign language vocabulary development opportunities. The purposes of this study were to determine the key demographic characteristics of educational interpreters in British Columbia, to identify the resources they use to learn new vocabulary, and to shed light on their Internet use and access levels, with a view to exploring the viability of this resource as a tool for vocabulary development for interpreters working in educational settings. Key demographics associated with interpreters' access to time and materials in advance of a lesson were job title and graduation from an interpreter training program. Interpreters with job titles that reflected their status as interpreters had more preparatory time each week than interpreters who had job titles focused on their roles as educational assistants. Interpreters overwhelmingly expressed the need for continuing professional development with respect to vocabulary development. In terms of the resources currently used, human resources (colleagues, deaf adults) were used significantly more often than nonhuman (books, videotapes, Internet). The resource use results showed that convenience was more important than quality. Books were used more often than videotapes, CD-ROMs, and the Internet, although the latter three had higher percentages of very satisfied users than did books. The design and content of online vocabulary resources and limited interpreter preparation time were identified as current issues keeping the Internet from reaching its potential as an easily accessible visual resource. Recommendations aimed at enhancing the viability of the Internet as a vocabulary development tool for educational interpreters are discussed.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".