Predicting risk for oral and written language learning difficulties in students educated in a second language
Bibliographic record
Abstract
ABSTRACT The extent to which risk for French as a second language (L2) reading and language learning impairment are distinct and can be predicted using first language (L1) predictors was examined in English-speaking students in total French immersion programs. A total of 86 children were tested in fall of kindergarten, spring kindergarten, and spring Grade 1 using an extensive battery of L1 predictor tests (in kindergarten) and L2 outcome tests (in Grade 1). Analyses of the kindergarten predictor scores revealed distinct underlying components, one related to reading and one to oral language. Further analyses revealed that phonological awareness, phonological access, and letter-sound knowledge in L1 were significant predictors of risk for reading difficulties in L2 while performance on L1 sentence repetition, phonological awareness, and tense marking tests in kindergarten were the best predictors of risk for L1 and L2 oral language difficulties. Both fall- and spring-kindergarten predictors predicted Grade 1 outcomes to a significant extent, with the spring-kindergarten predictors being more accurate. These results provide support for distinctive risk profiles for L2 oral language and reading difficulty and, furthermore, argue that assessment of L1 abilities can be used to make reasonably accurate predictions of later reading and/or oral language learning difficulties in L2 students.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".