Predicting writing development in dual language instructional contexts: exploring cross‐linguistic relationships
Bibliographic record
Abstract
This study examined whether decoding and linguistic comprehension abilities, broadly defined by the Simple View of Reading, in grade 1 each uniquely predicted the grade 6 writing performance of English-speaking children (n = 76) who were educated bilingually in both English their first language and French, a second language. Prediction was made from (1) English to English; (2) French to French; and (3) English to French. Results showed that both decoding and linguistic comprehension scores predicted writing accuracy but rarely predicted persuasive writing. Within the linguistic comprehension cluster of tests, Formulating Sentences was a strong consistent within- and between-language predictor of writing accuracy. In practical terms, the present results indicate that early screening for later writing ability using measures of sentence formulation early in students' schooling, in their L1 or L2, can provide greatest predictive power and allow teachers to differentiate instruction in the primary grades. Theoretically, the present results argue that there are correlations between reading-related abilities and writing abilities not only within the same language but also across languages, adding to the growing body of evidence for facilitative cross-linguistic relationships between bilinguals' developing languages.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".