Disability Representation in Saudi English Language Textbooks
Bibliographic record
Abstract
The aim of the present study was to investigate how people with disabilities are portrayed in the textbooks for English as a foreign language (EFL) that are currently used by students in Grades 1–12 in Saudi Arabia. A qualitative content analysis (texts and images) was employed to identify and critically analyze the textual and visual representations in the selected textbooks. Little research has examined the representation of people with disabilities in Saudi Arabia textbooks. Thus, this study aims to address this research gap and to contribute to the existing literature. The findings revealed that, while physical disabilities were the most frequently represented form of disability, sensory, cognitive, and learning disabilities were significantly underrepresented. The analysis also highlighted the extremally limited representation of disability in both textual and visual content, with only 0.45% of the total pages across all textbooks featuring disability-related content. Of note, the findings revealed that primary-level textbooks did not contain any representations of disability. Although the textbooks avoided negative stereotypes and presented people with disabilities in a positive light, the narrow focus on physical disabilities failed to capture the full spectrum of disability experiences. Therefore, the current representations of disability in Saudi EFL textbooks are limited and do not adequately reflect the diversity of disability experiences. Recommendations are provided for curriculum developers and policymakers to enhance the inclusivity of educational materials, to ensure that they reflect the diverse experiences of people with disabilities, and to promote content that is more inclusive.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".