The sign language proficiency interview: description and use with sign language of the Netherlands
Bibliographic record
Abstract
Presentatie op congres \nThe Sign Language Proficiency Interview (SLPI) is a tool for assessing functional sign language skill. Based on the Language Aptitude Test, it uses a recorded 20 minute conversation between a skilled interviewer and the candidate. The interview uses an ad hoc series of probing and challenging questions to elicit the candidate’s best use of the sign language in topics relating to the candidate’s work, family/background, and leisure activities. This video language sample is then analyzed to determine the candidate’s rating on the SLPI Rating Scale. The rating process documents vocabulary, grammar and discourse, and follows a specified protocol that includes specific examples from the interview. The SLPI is used widely in the US and Canada with American Sign Language, and one of the presenters has adapted it for use with South African Sign Language. \nThe presenters have recently adapted the SLPI for use with Sign Language of the Netherlands (NGT). While the interview process is the same regardless of the sign language, two aspects of the adaptation for NGT required work: 1) modifying the grammar analysis to match NGT grammar; and 2) modifying the Rating Scale to align with that of the Common European Framework of Reference for languages (CEFR). \nThis ICED presentation will include: 1) a thorough description of SLPI goals, processes and implementation; 2) modifications for NGT grammar; and 3) modifications to align with the CEFR.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.009 |
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".