Bibliographic record
Abstract
Abstract Sign language assessment tools are important for professionals working with DHH children to measure sign language development and competence. Adaptation of an existing test can be a solution when initiating assessment in a sign language community; the adaptation process must adhere to key principles and procedures. We introduce the principles of test adaptation and outline the challenges we faced in adapting the British Sign Language Production Test ( Herman, Grove, Holmes, Morgan, Sutherland & Woll 2004 ) to German Sign Language and American Sign Language. Challenges included decisions regarding the normative sample, the use of terminology, and variations in the scoring protocols to fit with each language. The steps taken throughout the test adaptation process are described, together with a comparison of parallels and differences. We conclude that test adaptation is an effective method of developing practical tools for sign language assessment and contributes to a better understanding of sign language development.
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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 | 0.204 | 0.395 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.012 | 0.010 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".