The development of basic reading skills in children: a cross-language perspective
Bibliographic record
Abstract
This chapter reviews recent empirical evidence for universal and orthography- or language-specific processes in the development of basic reading skills in school age children, suggesting that universal and orthography- or language-specific processes should be considered in tandem. The review focuses on three different aspects of reading, phonological processing, rapid naming, and morphosyntactic complexity, targeted in recent research on development of word recognition skills. Studies on L1 school children and studies of children who learn to read concurrently in their L1 and/or in a second language (L2) are examined within the context of variations in orthographic transparency. When children learn to read, characteristics of the spoken language interact with characteristics of the orthography. The chapter concludes that (a) individual differences in phonological processing skills, verbal memory, and rapid naming predict the development of reading in L1 and L2 children in various alphabetic and nonalphabetic languages; and (b) individual differences on such prerequisite skills can indicate smooth or problematic acquisition of L2 reading skills in children, regardless of oral language proficiency. However, task demands associated with learning to read in different orthographies vary and yield steeper or more moderate learning slopes. Regardless of the language and orthography combinations under study, children can develop reading strategies that help them read.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".