Syllabic Schemes and Knowledge of the Alphabet in Reading Acquisition: Onset or Nucleus Variation
Bibliographic record
Abstract
Although there is a growing consensus that, in reading acquisition, it is essential to provide children with learning activities that promote the development of reading cognitive schemes, particularly intra-syllabic related patterns, there is no agreement on which kind of syllabic schemes should be worked out in the first place. The main aim of the present study is to analyse the readings of preschool Spanish-speaking children showing the development of syllabic schemes in the early stages of reading acquisition. Basically, we analyse their responses in relation to their previous knowledge of Spanish grapheme-phoneme correspondences (GPCs) or alphabet knowledge. Our results show that children’s recognition and construction of syllabic schemes, from the very first steps in preschool reading acquisition programmes, is facilitated by reading activities presenting shell-nucleus syllabic patterns, for which the only requirement, although not indispensable, is to know the five or six Spanish vowel GPCs. This kind of activity seems to be more adequate than reading drills involving onset-rhyme syllabic analogies that require previous knowledge of consonant GPCs. The conclusion we have reached is that the development of onset-rhyme syllabic reading schemes shows a stronger relation to alphabet knowledge that shell-nucleus syllabic reading schemes, at least in the early stages of reading learning.
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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 | 0.003 |
| 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) | 0.007 | 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".