The Profile of Multiple Language Proficiencies: A Measure for Evaluating Language Samples of Deaf Children
Bibliographic record
Abstract
This article reports the process of creating a developmental measure that assesses the multilingual capabilities of deaf children and the problems that were encountered. Because deaf children may be using more than one method of communication (e.g., sign language skills and spoken language skills), it is important to evaluate their skills as completely as possible. In a pilot study, we used a nominal scale that assessed language skills based on a single continuum, with good English and good American Sign Language (ASL) skills as its two extremes and approximately equal skills in both as the midpoint. In the main study, a more complete measure was created, the Profile of Multiple Language Proficiencies (PMLP). The PMLP uses a single scale that represents the different stages of language development that can be observed in both English and ASL. The PMLP showed reasonable initial reliability and has good promise as an easy-to-use measure of developing language skills in children who use multiple modalities of communication. Using the PMLP as a prototype, we discuss some of the issues that influence the reliability and validity in evaluating such a scale and how these can be overcome or avoided.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".