Complicating the Narrative: Representation, Marjane Satrapi’s Persepolis, and Witnessing
Bibliographic record
Abstract
Refugee and forced migrant issues have emerged as part of the nation’s consciousness with the rising number of asylum seekers arriving in European countries, the uncertainty over the position of undocumented migrants in the United States, and the increase in asylum seekers crossing the United States-Canada border. The limited space and opportunity refugees and forced migrants have to represent themselves can lead to generalizations and over-simplified narratives about their lived experiences (O'Neill, 2008). Focusing on Persepolis by Marjane Satrapi (2003), this paper examines how graphic narratives written by forced migrants can complicate generalizations and act as a witness to their experiences. Simon’s (2005, 2014) writing frames my thinking on witnessing and Hall’s (1997a, 1997b; Jhally, 2014) ideas of representation, meaning, and power frame how I think about the representation of forced migrants. I explore the following questions in this paper: How does Persepolis bear witness to the complexity of Satrapi’s experiences of forced migration? What might a possible response to this work of witness look like?
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.033 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".