The Representations of Islam and Muslims in popular media: Educational Strategies and to develop critical media literacy
Bibliographic record
Abstract
ABSTRACT \n \nThe way Islam is understood today, and for much of the Western world, is based on the perception that was established by Orientalist scholars of the 18th to 20th century. Many studies have demonstrated that the negative images of the Muslim world, in American Western mass media, particularly Hollywood movies, are inherited attitudes from the old ‘guild tradition’ school of Orientalism. As the matter of fact, these biased attitudes, which still in some ways dominate the Western way of thinking, are deeply rooted in the history of colonialism and orientalist scholarship. Today, American mass media and particularly Hollywood is taking these inherited misconceptions of the Muslim World and representing it to the world in a new format. \nThis study examines the representations and portrayal of Islam and Muslims in American Popular Culture, especially Hollywood movie productions. The findings indicate that Islam and Muslims received negative coverage. A consistent stereotyped association with violence, terrorism, fundamentalism and extremism marks the representations of Islam and Muslims in Hollywood movies. These representations encapsulate the perception of Islam and Muslims by mass media to the point it becomes very difficult to perceive Islam and Muslims differently. \nThe study also attempts to examine the role of education in demystifying the negative representations of Islam and Arab Muslims in Popular Culture. Moreover, it demonstrates that the critical study of these misrepresentations in the American popular culture may contribute towards establishing a more democratic, peaceful, and just world.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".