Morphological awareness and reading comprehension: to what extent do semantic relations in the classic sentence completion task influence associations?
Bibliographic record
Abstract
Abstract It is well-established that morphological awareness is related to reading comprehension. Morphological awareness is often assessed with a sentence completion task, in which children are asked to complete a sentence with a related word (e.g., “warm. He chose the jacket for its __”). As evident from this classic example, semantic relations could influence performance because warmth is related in meaning to jacket . We examine whether the degree of semantic relations in the sentence completion task influences the association between morphological awareness and reading comprehension. In grade 3, English-speaking children did a sentence completion task in two conditions: one with sentences designed to have high semantic relations with the target and another with low. Children also completed control measures of non-verbal reasoning, vocabulary, phonological awareness, working memory, and word reading fluency. At grade 4, children completed reading comprehension. Hierarchical regression analyses showed that performance on both conditions of the sentence completion task (i.e., high and low semantic relations) significantly predicted reading comprehension, after all controls. Intriguingly, when both tasks were in the same regression, only performance on the high semantic relations task made a unique contribution to reading comprehension. The findings confirm the contribution to reading comprehension of morphological awareness, assessed with the sentence completion task, and show the relevance of semantic dimensions to these relations. As such, findings appear to validate the use of sentence completion to assess morphological awareness and highlight its capture of the multidimensional nature of morphological awareness, including its semantic dimensions.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".