Learning to read and spell in Persian: A cross-sectional study from Grades 1 to 4.
Bibliographic record
Abstract
We investigated the reading and spelling development of 140 Persian children attending Grades 1-4 in Iran. Persian has very consistent letter-sound correspondences, but it varies in transparency because 3 of its 6 vowel phonemes are not marked with letters. Persian also varies in spelling consistency because 6 phonemes have more than one orthographic representation. We tested whether lexicality effects-an advantage of words over nonwords-would be affected be reading transparency and spelling consistency. We found that children became more efficient readers and spellers across grades, with the greatest growth occurring between Grades 1 and 2. For reading, lexicality effects were present with transparent words starting in Grade 2, but lexicality effects with opaque words were not yet present in Grade 4. As expected, the size of transparency effects for reading decreased across grades. For spelling, however, there was no lexicality effect for either consistent or inconsistent words. Moreover, consistency effects were large and did not decrease systematically across grades. Most interesting from a developmental perspective was the finding that both reading transparency and spelling polygraphy affected reading as well as spelling in Grades 1 and 2, but the word characteristics had differential effects as a function of literacy task in Grades 3 and 4. This pattern highlights the vulnerability of children's representations and processes during the early phases of acquisition as well as the rapidity with which representations and processes become specialized as a function of the literacy task at hand.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.001 |
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".