Linguistic and Literacy Predictors of Early Spelling in First and Second Language Learners.
Bibliographic record
Abstract
Abstract Error analyses using a multidimensional measure were conducted on the misspellings of Kindergarten children speaking English as a first (EL1) and English as a second language (ESL) in order to detect any differences in early spelling ability between language groups. Oral vocabulary, syntactic knowledge, phonological processing, letter/word reading, and alphabet writing fluency tasks were also administered. The speakers of EL1 and ESL achieved similar spelling sophistication scores despite lower oral vocabulary and syntactic knowledge for the ESL group. More sophisticated spelling approaches were related to better-developed phonological processing, syntactic knowledge, and early word reading irrespective of language status. Résumé Afin de détecter des compétences orthographiques précoces différentes, une analyse multidimensionnelle des erreurs d’orthographe s’est effectuée chez des élèves en maternelle dont l’anglais est la langue natale (AL1) et ceux dont l’anglais est une langue seconde (AL2). On a également testé le vocabulaire à l’oral, l’alphabet à l’écrit, les connaissances syntaxiques, la procédure phonologique et la lecture des lettres et des mots. Malgré un vocabulaire à l’oral plus restreint et moins de connaissances syntaxiques chez les élèves AL2, les deux groupes atteignaient des scores de sophistication orthographique pareils. Indépendamment de la langue maternelle, les démarches orthographiques plus sophistiquées correspondaient à une procédure phonologique plus avancée, aux connaissances syntaxiques et à la lecture précoce des mots.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".