Morphological Awareness and Prosodic Sensitivity in the Development of Advanced Reading Skills
Bibliographic record
Abstract
That language abilities and literacy abilities are intrinsically linked is a well-founded conclusion, driven by the past three decades of research examining reading development. Although the effects of phonological awareness (PA) - the conscious ability to manipulate the sound structure of one’s native language - in developing successful early reading skills are well-known, its predictive abilities attenuate rapidly as development progresses. Accordingly, more recent research has also examined the influence that other linguistic skills present. The present study examines how morphological awareness (MA) - the conscious understanding of how words can be created by using different morphemes, the meaningful units of language - and prosodic sensitivity (PS) - the perception of how stress patterns in English can change the meaning of a word or phrase - affect the reading skills of children in grades 3, 5, and 7 from the Kingston area. Each child was given three batteries of tests, comprised to measure the child’s abilities in reading, MA, PS, PA, language comprehension, memory, general intelligence, and other skills. Our results show that both morphological awareness and prosodic sensitivity are significant predictors of reading skills, above and beyond the significance of phonological awareness, and after controlling for other skills such as memory and intelligence. Such findings are critical to improving our understanding of how reading ability develops in children and how we as researchers may be of aid to improving the skills of children struggling to learn literacy skills.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".