Bibliographic record
Abstract
Since the 2022 death of Mahsa Jina Amini in custody of the Guidance Patrol or morality police in Tehran, Iran, Persepolis by Marjane Satrapi can also function in the classroom as a comics touching point for human rights discourses around the world and in particular—though not exclusively—those that impact women. Kimberlé Crenshaw, who brought intersectionality to the forefront of cultural and political discourses in 1989, has used the phrase “say her name” to draw attention to the deaths of women and children, especially Black women and children, at the hands of law enforcement officers. Chants of “Say her name, Mahsa Amini”, rang among protesters outside Khalifa International Stadium in Qatar ahead of Iran’s first match of the World Cup 2022 against England. Now in 2025, cultural conversations around feminism and creativity as resistance can turn to the woman, life, freedom movement in Iran. Shervin Hajipour’s song “Baraye”, meaning “for” in Persian, which was inspired by tweets echoing protesters’ calls for change, became an anthem of the uprising and exists in comic art as well as song. The comics classroom can address the concerns and issues surrounding Amini’s death and the ongoing relevance of Persepolis as a coming-of-age text about living as a woman in Iran. In dialogue with the works of Sidonie Smith, Julia Watson, Hillary Chute, Sally Munt, and bell hooks, this piece addresses the pedagogy of human rights through comic art as crisis witnessing. With attention to comics material from two members of the Iranian diaspora, Shabnam Adiban and Farid Vahid, from the 2024 collection Woman, Life, Freedom, put together by Satrapi, this piece navigates potential Orientalism and Islamophobia in the Western classroom through engagement with intersectional feminism.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.013 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".