Reading Arabic Shallow and Deep Genres: Indispensible Variables to Science of Reading
Bibliographic record
Abstract
Most principles and propositions in the science of reading are derived from research on Latin orthographies,usually, in English while much less is known about Semitic orthographies, namely, Arabic. This studyinvestigated the effect of vowels and type of genre on oral accuracy, oral rate, and oral comprehension in readingArabic orthography. A convenience sample of 85 children (34 fifth male graders and 51 tenth male graders) wasselected from two public schools in Saudi Arabia. The researcher developed two reading measures; the FifthGrade Reading Measure and Tenth Grade Reading Measure. Each measure has two genres (informational andpoetic) and two versions (shallow/vowelized and deep/unvowelized). Each child individually completed the twoversions of the measure in his grade. The results revealed that the students read the shallow genres(informational and poetic) more accurately and with more comprehension but less rapidly than reading the deepgenres. In addition, the students read the informational genre (shallow and deep) more accurately, rapidly, andwith more comprehension than the poetic genre (shallow and deep). The discussion concludes that a) the natureof Arabic orthography, mainly vowels, is an indispensible variable to the literature of science of reading, b) oralreading accuracy, oral reading rate and oral reading comprehension are affected by the unique characteristics ofthe genre, and c) vowels in Arabic are important to improve oral reading accuracy, and oral readingcomprehension for the first grades in primary school and later grades in secondary school as well.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".