Bibliographic record
Abstract
Abstract Many scholars cite Edward Said’s concepts of Orientalism and Othering that drew on West-East binaries that position the West as civilized and the East as barbaric. Such Othering is represented in various Western media through the association of Muslims and Islam with violence, fundamentalism, and terrorism. While such representations were being developed within public discourse through early mass media during the post–World War II period, such discriminatory and reductionist representations of Muslims were more clearly seen within the US media during the Iranian Hostage Crisis and Gulf War periods. Said effectively outlines the various ways in which news coverage presented Arabs and Muslims as an imagined Other, reduced to specters of “Islam”—a constructed idea of the religion as violently opposed to American rights, values, and democracy. There is no doubt that the largest shift in nuance in the public discourse and media representation of Muslims occurred after the terrorist attacks of September 11, 2001, with hate speech, violence, racism, and fear toward Muslim communities increasing across North America. The main discourses on the Muslim communities in North America continued to be Orientalist in nature but more obviously racialized regardless of the varied geographical regions and ethnic heterogeneity of the peoples that make up these various Muslim groups. As a result, Islam itself was racialized and generally used as a pretext to oppress Muslims. Other major themes that emerge from the news coverage of Islam in North America include Islam as being oppressive, and the gendered representations of Muslims, especially veiled women as needing to be rescued.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".