A Retrospective Analysis of Cultural Patriarchy in Majid Rafizadeh’s A God Who Hates Women: A Woman’s Journey Through Oppression
Bibliographic record
Abstract
Cultural patriarchy is the male dominance practiced in the name of culture in a society. Majid Rafizadeh, an Iranian American author, businessman, political scientist, and academician, is known for his distinguished writings on gender equality in Middle Eastern regions. His famous novels are A God Who Hates Women: A woman’s journey through oppression and My Story of Child Marriage. The present study has chosen Majid Rafizadeh’s autobiographical novel, A God Who Hates Women: A woman’s journey through oppression, to determine a retrospective analysis of cultural patriarchy. The methodology of the present study uses textual analysis from qualitative research to analyze the selected novel. The current research emphasizes that the oppression of women is due to male dominance. Hence, the culture of that particular society instills this dominance in the minds of men. It is evident in the selected novel. Society’s culture determines the roles and performances of men and women in both private and public areas. Thus, culture influences people’s collective consciousness. Religion, on the other hand, instructs the people to follow a code of conduct to protect their cultural heritage. Furthermore, the current study analyzes the chosen novel using Judith Butler’s gender performativity theory and Raymond William’s dominant, residual, and emergent cultural concepts. The current research is an attempt to analyze cultural patriarchy to propose a solution for achieving gender equality in society.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".