Portraits of Otherness: Arabs and Muslims in Homeland and Little Mosque on the Prairie
Bibliographic record
Abstract
The U.S. media has often projected Muslims and Arabs in the West negatively, especially after the 9/11 attacks. The negativity identifies Arabs as the “other.” Through numerous symbols of difference, the U.S media helps to create a clear “us versus them” binary imposing homogeneous images of Muslims as “terrorists,” “patriarchal abusers of women,” and “tyrants.” Drawing from literature in religious and gender studies in the framework of Marxistpsychoanalysis, this thesis analyzes the American political thriller Homeland (2011) in contrast to the Canadian sitcom Little Mosque on the Prairie (2007). Written by a Muslim woman, Little Mosque creates humors out of the conflicting and complicated diversities and realities of being Muslim, while Homeland demonizes Arabs and Muslims. By employing Žižek’s concepts in The Sublime Object of Ideology, I explore how fantasies shapes human society. Žižek’s analyses help to clarify the idea that people’s fantasies are the basis of popular cultures. These fantasies in turn shape the society’s biases and these biases shape individuals’ conceptions. However, while Homeland reinforces the racist ideology and fantasy, Little Mosque challenges it. Little Mosque converts the standard monochromatic stereotypes into a diverse mosaic, thereby moving Muslims from the realm of static otherness to one of dynamic conflicts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".