Evaluation of the Crosslinguistic Nonword Repetition Test: Evidence From a Large and Diverse Secondary Data Set
Bibliographic record
Abstract
PURPOSE: The aim of this study was to evaluate the crosslinguistic validity of the Crosslinguistic Nonword Repetition Test (CL-NWR) based on a large multicountry sample by investigating factors related to language ability, as well as potential confounds. METHOD: The data consisted of CL-NWR scores from children aged 37-165 months, collected by 18 research teams across 15 countries. Item-level analysis was employed to examine any nondesirable effects of gender, socioeconomic status, bilingual status, and the amount of exposure to the test language, as well as desirable effects of age, item length, and clinical status (children categorized as typically developing [TD], with developmental language disorder [DLD], or with reported language concerns [LC], respectively). Subsamples were used to evaluate the consistency of findings across three time points and between different versions of the CL-NWR. RESULTS: Bayesian analysis provided strong evidence for the effects of age, item length, and clinical status on CL-NWR performance, as well as consistency across time points. In contrast, there was weak or no evidence for the effects of gender, socioeconomic status, bilingual status, amount of exposure, or test version. Additionally, there were two interactions between (a) item length and clinical status, suggesting that children with DLD found longer nonwords disproportionately more challenging than TD children, and (b) age and clinical status, with the gap between TD and LC groups narrowing with age. CONCLUSIONS: The CL-NWR was unaffected by environmental and demographic factors that often influence language assessments, including some nonword repetition tests. Performance was driven by factors reflecting language abilities. This makes the CL-NWR a unique and valuable tool for language assessment contributing to the identification of DLD in diverse linguistic, social, and geographical contexts.
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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 | 0.028 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".