Factors and Reasons Behind Disappearance of Arab Women in Arabic Literature From the Classical Era to the Modern One
Bibliographic record
Abstract
This article is about the reasons and the factors that mainly led to many terrible effects on Arab women diminution in Arabic literature, such as the tribal conflicts, Umayyad caliphate and its battle against Abbasids caliphate after death of Muhammad, marrying women for pleasure and treating them as a sexual tool, in the Arabian Peninsula beginning from the pre- Islamic era to the modern era of Arabic literature. Furthermore, it also clarifies many women situations during those eras and their participations in many different events in battles by healing the injured people, helping their husbands in order to stop Muhammad’s teachings and his tradition regardless paying attention to pursue education and learn poetry due these chippy reasons, resulting in many bad education and increasing level of analphabetism quickly in addition to their situations during the great engagement which happened between Umayyad dynasty and Abassids dynasty. It also elucidates how technology and revolution of information in the twentieth century, as well as after the second world war, led to many great results on Arab women’s appearance, lately, especially in Saudi Arabia in media, press, drama, journalism and their involutions on developing Arabic literature including all its literary forms as well as their contributions of children literature appearance during the twentieth century.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".