The Representation of Women in Saudi Film: The Case of Amra and the Second Marriage
Bibliographic record
Abstract
Female representation in film continues to be a new area of research in Gulf countries, particularly Saudi Arabia. Mahmoud Sabbagh, the writer and director of the Netflix film Amra and the Second Marriage, has portrayed Saudi women in various ways. His work was recognized with the Best Film award at the Cinematic Festival of Film Across Three Continents in Milan. The current study examines female stereotypes as portrayed in this film, employing a mixed-method approach of quantitative content analysis and qualitative thematic analysis. The study’s conceptual framework draws on representation theory and feminist theory to explore the depiction of female stereotypes in this digital Saudi film. A total of 20 females were recruited in this study from the online film. The findings revealed that Amra and the Second Marriage represented twenty Saudi female characters, with half of them being mature adults. Approximately 40% of these characters were portrayed as modern, and around 35% were depicted as belonging to the upper class. Less than half of the characters were shown without wearing the hijab. The film also depicted several stereotypes of Saudi women as weak and sacrificial, reinforcing negative stereotypes about Saudi culture as traditional and masculine. Furthermore, the movie showcased both ‘religious’ and ‘gossip’ stereotypes that mirror aspects of Saudi culture. Interestingly, the film also explored the theme of ‘corrupt women,’ a less common portrayal in Saudi culture.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".