Learning Orthographic and Semantic Representations Simultaneously During Shared Reading
Bibliographic record
Abstract
Purpose The value of shared reading as an opportunity for learning word meanings, or semantics, is well established; it is less clear whether children learn about the orthography, or word spellings, in this context. We tested whether children can learn the spellings and meanings of new words at the same time during a tightly controlled shared reading session. We also examined whether individual differences in either or both of orthographic and semantic learning during shared reading in English were related to word reading in English and French concurrently and 6 months longitudinally in emergent English–French bilinguals. Method Sixty-two Grade 1 children (35 girls; M age = 75.89 months) listened to 12 short stories, each containing four instances of a novel word, while the examiner pointed to the text. Choice measures of the spellings and meanings of the novel words were completed immediately after reading each set of three stories and again 1 week later. Standardized measures of word reading as well as controls for nonverbal reasoning, vocabulary, and phonological awareness were also administered. Results Children scored above chance on both immediate and delayed measures of orthographic and semantic learning. Orthographic learning was related to both English and French word reading at the same time point and 6 months later. In contrast, the relations between semantic learning and word reading were nonsignificant for both languages after including controls. Conclusion Shared reading is a valuable context for learning both word meanings and spellings, and the learning of orthographic representations in particular is related to word reading abilities. Supplemental Material https://doi.org/10.23641/asha.13877999
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".