Home language variation in the narratives of urban First Nations Australian children in their first year of school
Bibliographic record
Abstract
First Nations children may speak a dialect of English that has different grammatical rules from Standard Australian English (school language). Limited studies have investigated Aboriginal English (home language) dialect in First Nations children and its impact on differential diagnosis of language disorder. This study measured the density of home language dialect and grammatical accuracy in oral narratives produced by typically developing First Nations children. Non-standardised assessment narrative protocols were used to elicit language samples from 27 Australian First Nations children aged 4.5-6 years. Local home language dialectal features were coded into the sample and grammatical accuracy was calculated separately for school language and home language. All children displayed some use of home language features. The most common home language features used were alternative use of regular past tense and irregular past tense, zero use of regular and irregular past tense, and alternative use of pronouns. Dialect density varied highly amongst participants. Grammatical accuracy was higher for home language than school language. Speech pathologists and teachers need to be aware of differences between home and school language for First Nations children to avoid misdiagnosis of language disorder. More research is required to gain normative data that informs culturally appropriate assessment practices for this population.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".