Bibliographic record
Abstract
The Internet is one of the most successful means of providing a rich learning environment, and children are among the most affected by that interactive atmosphere. The research adopted a descriptive approach using an evaluation card in a checklist form to evaluate general, educational content, and technical elements of Arabic- language children’s websites. The evaluation card included (17) domains and (127) items examining (20) Arabic websites for children representing the research sample. In terms of general elements, the research findings confirmed that accessibility and ease of use received the highest rating (93.33%), while continuous timeliness was the lowest (21.25%). In the elements of educational content, the written text was the highest (91.11%), while the interactivity was only (30.77%), and finally the technical elements, the written text at the highest ratings (93%), compared to video, animation and sound at the lowest rating (60.83%). Regarding the availability of key domains in the websites, technical elements received the highest percentage (71%), followed by general elements (62.58%), and the educational content elements were last (53.93%). The research main recommendations involved designing Arabic websites for children with their interests and developmental needs as well as benefiting from websites in the international settings, by avoiding the shortcomings and weaknesses revealed in the research results.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".