Orthographic and phonological processing skills in reading and spelling in Persian/English bilinguals
Bibliographic record
Abstract
The concurrent development of reading and spelling in English and Persian were examined in a sample of bilingual children. The objective was to compare how phonological and orthographic processing skills contribute to reading and spelling for two alphabetic languages that differ drastically. English orthography is characterised by both polyphony (i.e., a grapheme representing more than one phoneme) and polygraphy (i.e., a phoneme represented by more than one grapheme) which results in a complex script to read and write. In contrast, vowelised-Persian orthography is characterised by polygraphy only, which results in a simple script to read but more complex to write. Fifty-five Iranian children in grades 2 and 3, who had lived in English-speaking Canada for an average of 4 years, were tested on word reading and spelling in English and Persian. We found that the predictors of reading performance were similar across languages: Phonological and orthographic processing skills each predicted unique variance in word reading in English and in Persian once we had controlled for grade level, vocabulary, and reading experience. As expected, the predictors of spelling performance differed across language: Spelling in English was predicted similarly by phonological and orthographic processing skills, whereas spelling in Persian was predicted by orthographic processing skills only. It is possible that the nature of the Persian orthography encourages children to adopt different strategies when reading and spelling words. Spelling Persian words might be particularly conducive to using an analytic strategy which, in turn, promotes the development of and reliance on orthographic 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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".