Using language sample analyses across English dialects: A case-based approach for preschoolers
Bibliographic record
Abstract
This study compared language samples from typically developing 4-year-olds who spoke African American English (AAE), Jamaican English (JE), or Mainstream American English (MAE) to assess the value of using language sample analysis (LSA) measures for characterising language use across dialects of English. Specific LSA metrics included mean length of utterance (MLU) in morphemes and in words, the Index of Productive Syntax (IPSyn), Developmental Sentence Scoring (DSS) and measures of lexical diversity. Children demonstrated diverse linguistic patterns across dialects, but a Kruskal-Wallis H test did not reveal significant differences in scores obtained through LSA measures. Notably, the IPSyn captured morphosyntactic structures in each category across dialects where prior research has highlighted limitations. This preliminary study uses a case-based approach to illustrate the applicability of LSAs in describing linguistic variations across children who speak different dialects of English. Moreover, the findings from this study underscore the potential use of LSAs in describing linguistic patterns to support the characterisation of communication profiles for culturally and linguistically diverse children.
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 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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".