Predictors of Literacy and Attitudes Toward Reading Among Syrian Refugee Children in Jordan
Bibliographic record
Abstract
Abstract Refugee children often face disruptions to their education before and during displacement. However, little is known about either levels or predictors of refugee children’s literacy or about their attitudes toward reading in low- or middle-income countries. To address this, we conducted in-home literacy assessments using the Holistic Assessment of Learning and Development Outcomes with 322 Syrian refugee mother–child dyads who lived in Jordan (child age range 4–8 years, M = 6.32 years, 50% female). Overall, the children had quite low levels of literacy, although they indicated a strong enthusiasm for reading. Child age, maternal education, and maternal ability to read all predicted child literacy, although maternal literacy predicted it only among children enrolled in school. Among those enrolled in school (64.9% of the total sample, 88.7% of those aged ≥ 6), students attending hybrid classes had better literacy than those attending either solely in-person or solely online, although the frequency of school attendance did not predict literacy. A less consistent pattern emerged for predicting children’s attitudes toward reading. Our results suggest an urgent need to improve literacy skills among refugee children in Jordan, as well as a need for validated measures of attitudes toward reading for use with Arabic-speaking youth.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".