Arab Americans in Film: From Hollywood and Egyptian Stereotypes to Self-Representation
Bibliographic record
Abstract
What can film studies bring to the study of Arab culture, politics, and history? The past ten years have seen an increase in historical, theoretical, and methodological exchanges between Middle East studies and film and media studies. The sub-field of “Arab film studies” (Ginsberg and Lippard 2020, viii) has emerged as one possible intersection of these two fields of inquiry. This is illustrated by two recent book series, the Cinema and Media Cultures in the Middle East series at Peter Lang Publishing (edited by Terri Ginsberg and Chris Lippard) and the Palgrave Studies in Arab Cinema series at Palgrave Macmillan (edited by Nezar Andary and Samirah Alkassim). Waleed Mahdi's Arab Americans in Film (2020) and Peter Limbrick's Arab Modernism as World Cinema: The Films of Moumen Smihi (2020) consolidate these exchanges across ethnic studies, area studies, political sciences, (art) history, and film and media studies. While Mahdi primarily positions himself from within ethnic studies and Limbrick is first a film scholar, both have published in reference journals in film studies, Middle East studies, and cultural studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".