Profiles of Poor Decoders, Poor Comprehenders, and Typically Developing Readers in Adolescents Learning English as a Second Language
Bibliographic record
Abstract
This study examined (a) the identification of various reading groups across languages in Chinese (L1) adolescents learning English as a second language (ESL), in terms of their word-reading and reading comprehension skills, (b) overlap in reading group membership across languages, and (c) the performance of the various reading groups on reading-related language comprehension measures in English. The participants were 246 eighth-grade students from an English-immersion program in a middle school in China. Latent profile analysis identified three reading groups in each language: (a) a typically developing reader group with average or above-average word-reading and reading comprehension, (b) a group with poor decoding/word-reading skills and weak reading comprehension, and (c) a group with poor reading comprehension in the absence of poor decoding/word reading. The overlap in profile characteristics across languages for typically developing readers and poor decoders was high (about 68% for typically developing readers and 54% for poor decoders), whereas the overlap for being poor comprehenders in each language was moderate (about 37%). Furthermore, poor decoders in either language performed more poorly than the typically developing and poor comprehender groups on word reading in the other language, while poor comprehenders in either language performed more poorly than the typically developing and poor decoder groups on reading comprehension in the other language. The comparison of the reading groups' performance on English reading-related language comprehension measures showed that poor comprehenders and poor decoders performed worse than typically developing readers. Implications for identification and instruction of ESL children with reading difficulties are discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".