Exploring The Dynamics of Intertextuality: A Study of Selected Works by Rajāʾ ʿĀlim
Bibliographic record
Abstract
RajāʾʿĀlim, a distinguished Saudi Arabian author, is often noted for her overtly complex and densely layered use of intertextual references, which renders her writing convoluted and intricate. However, this research argues that despite these criticisms, her intertextual approach ultimately enriches the literary experience, highlighting her significant impact on Saudi and Arabic literature through the lens of intertextuality.The study explores ʿĀlim's intertextual approach in various genres (novels, short stories, and drama), seamlessly integrating aesthetic and political elements for a captivating and intellectually stimulating reading experience. Through analyzing literary, religious, historical, and mythical references, the research reveals the profound implications of her intertextuality.The findings demonstrate that intertextuality serves as a powerful tool, expanding the scope of her texts and transcending boundaries, thus placing her work within a larger universal context. It fosters familiarity and connection with established texts, prompting readers to adopt innovative reading approaches that challenge the notion of self-contained literary works. This cultivates a diverse cultural discourse, bridging her work to a timeless and borderless literary heritage, and giving rise to fresh perspectives or commentaries on the role of literature within society, actively participating in pivotal contemporary discussions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".